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Relations",{"metadata":12161,"sys":12164,"fields":12174},{"tags":12162,"concepts":12163},[],[],{"space":12165,"id":12167,"type":14,"createdAt":12168,"updatedAt":12168,"environment":12169,"publishedVersion":940,"revision":1136,"contentType":12171,"locale":673},{"sys":12166},{"type":657,"linkType":658,"id":659},"37tNeJFfm5UwscGHspzzHD","2026-05-19T19:04:28.518Z",{"sys":12170},{"id":665,"type":657,"linkType":666},{"sys":12172},{"type":657,"linkType":671,"id":12173},"media",{"entryTitle":12175,"image":12176},"The Lie At The Center Of Enterprise RAG  - Feature Image",{"metadata":12177,"sys":12180,"fields":12187},{"tags":12178,"concepts":12179},[],[],{"space":12181,"id":12183,"type":934,"createdAt":12184,"updatedAt":12184,"environment":12185,"publishedVersion":847,"revision":1136,"locale":673},{"sys":12182},{"type":657,"linkType":658,"id":659},"3iN9hBpuXnngbYJW0XHX0O","2026-05-19T19:03:50.029Z",{"sys":12186},{"id":665,"type":657,"linkType":666},{"title":12188,"description":12188,"file":12189},"The Lie At The Center Of Enterprise RAG ",{"url":12190,"details":12191,"fileName":12194,"contentType":1460},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3iN9hBpuXnngbYJW0XHX0O\u002F33c5e8bf4eaebb7047ecc62527e27733\u002FCR-10229_BFI_Enterprise-_RAG_VAST-Data.jpg",{"size":12192,"image":12193},1343552,{"width":1144,"height":1145},"CR-10229_BFI_Enterprise- RAG_VAST-Data.jpg",{"data":12196,"content":12197,"nodeType":1922},{},[12198,12205,12212,12219,12226,12269,12276,12317,12324,12343,12349,12357,12364,12371,12378,12387,12394,12401,12407,12414,12421,12428,12435,12443,12450,12457,12463,12470,12477,12484,12491,12498,12505,12511,12518,12537,12544],{"data":12199,"content":12200,"nodeType":1921},{},[12201],{"data":12202,"marks":12203,"value":12204,"nodeType":1907},{},[],"Let’s imagine a manufacturing company for a moment. An employee asks about a product manual and the GPUs go chew on the necessary documents. An hour later, another employee asks a similar question but instead of using what it just surfaced, it goes and rebuilds the same context all over again.",{"data":12206,"content":12207,"nodeType":1921},{},[12208],{"data":12209,"marks":12210,"value":12211,"nodeType":1907},{},[],"Or imagine too a developer sitting down to open a session, loading the same repositories each time they start. Support teams repeatedly query the same manuals and ticket histories. Financial analysts reload the same filings and compliance documents into long-context systems.",{"data":12213,"content":12214,"nodeType":1921},{},[12215],{"data":12216,"marks":12217,"value":12218,"nodeType":1907},{},[],"More broadly, almost all agentic workflows revisit the same operational data over and over as tasks move between users and systems.There are countless possible examples of this in enterprise settings and the fact is clear, the big secret when it comes to how businesses use AI is that there is a constant loop of rebuilding the same context time and again.",{"data":12220,"content":12221,"nodeType":1921},{},[12222],{"data":12223,"marks":12224,"value":12225,"nodeType":1907},{},[],"Across the enterprise, huge amounts of compute are spent reconstructing context the system already understood yesterday and the real cost goes far beyond just retrieval as we would have thought of it in the before-times, it’s the expense of context (re)construction.",{"data":12227,"content":12228,"nodeType":1921},{},[12229,12233,12241,12245,12253,12257,12265],{"data":12230,"marks":12231,"value":12232,"nodeType":1907},{},[],"Now as enterprises want AI that moves toward ",{"data":12234,"content":12236,"nodeType":1916},{"uri":12235},"..\u002F..\u002Fblog\u002Faccelerating-inference",[12237],{"data":12238,"marks":12239,"value":12240,"nodeType":1907},{},[],"long-context inference",{"data":12242,"marks":12243,"value":12244,"nodeType":1907},{},[],", ",{"data":12246,"content":12248,"nodeType":1916},{"uri":12247},"..\u002F..\u002Fpress-releases\u002Fvast-data-and-nvidia-unlock-real-time-multimodal-ai-agent-intelligence",[12249],{"data":12250,"marks":12251,"value":12252,"nodeType":1907},{},[],"multi-document reasoning",{"data":12254,"marks":12255,"value":12256,"nodeType":1907},{},[],", and ",{"data":12258,"content":12260,"nodeType":1916},{"uri":12259},"..\u002F..\u002Fblog\u002Fintroducing-agentengine",[12261],{"data":12262,"marks":12263,"value":12264,"nodeType":1907},{},[],"agentic workflows",{"data":12266,"marks":12267,"value":12268,"nodeType":1907},{},[],", prefill has become one of the most expensive parts of inference. The system spends massive GPU resources ingesting documents, processing token relationships, and building KV cache before useful reasoning even begins, only to have much of that disappear when the session ends.",{"data":12270,"content":12271,"nodeType":1921},{},[12272],{"data":12273,"marks":12274,"value":12275,"nodeType":1907},{},[],"So the enterprise pays to rediscover the same information again and again. So, that lie referred to in the title? It’s that the infrastructure acts like every prompt is brand new even when the organization has already built the same contextual understanding thousands of times before.",{"data":12277,"content":12278,"nodeType":1921},{},[12279,12283,12291,12294,12302,12305,12313],{"data":12280,"marks":12281,"value":12282,"nodeType":1907},{},[],"It goes a bit deeper than this too. The industry treated retrieval as the hard problem, so it built ",{"data":12284,"content":12286,"nodeType":1916},{"uri":12285},"..\u002F..\u002Fresources\u002Fwhite-papers\u002Fvast-data-vector-database",[12287],{"data":12288,"marks":12289,"value":12290,"nodeType":1907},{},[],"vector databases",{"data":12292,"marks":12293,"value":12244,"nodeType":1907},{},[],{"data":12295,"content":12297,"nodeType":1916},{"uri":12296},"..\u002F..\u002Fblog\u002Fthe-end-of-the-unstructured-data-era",[12298],{"data":12299,"marks":12300,"value":12301,"nodeType":1907},{},[],"embedding systems",{"data":12303,"marks":12304,"value":12256,"nodeType":1907},{},[],{"data":12306,"content":12308,"nodeType":1916},{"uri":12307},"..\u002F..\u002Fblog\u002Fvast-catalog-treat-your-file-system-like-a-database",[12309],{"data":12310,"marks":12311,"value":12312,"nodeType":1907},{},[],"semantic layers",{"data":12314,"marks":12315,"value":12316,"nodeType":1907},{},[]," around finding things fast, and while those are important tools, retrieval wasn’t the expensive part, rebuilding understanding that was.",{"data":12318,"content":12319,"nodeType":1921},{},[12320],{"data":12321,"marks":12322,"value":12323,"nodeType":1907},{},[],"Most inference infrastructure still hasn’t caught up to this because even still, every request is treated as isolated even when the same documents, repositories, policies, tickets, and reports are being queried constantly across the organization.",{"data":12325,"content":12326,"nodeType":1921},{},[12327,12331,12339],{"data":12328,"marks":12329,"value":12330,"nodeType":1907},{},[],"The model retrieves the same information again and again, then rebuilds nearly the same context from scratch each time, with GPUs repeating the same prefill work, regenerating similar attention patterns, then reconstruct the same KV cache because the system has no durable memory of what it already processed earlier that day.This becomes especially wasteful at enterprise scale and as ",{"data":12332,"content":12334,"nodeType":1916},{"uri":12333},"..\u002F..\u002Fblog\u002Fkvcache-context-memory-storage",[12335],{"data":12336,"marks":12337,"value":12338,"nodeType":1907},{},[],"context windows grow larger",{"data":12340,"marks":12341,"value":12342,"nodeType":1907},{},[],", the inefficiency grows with them.",{"data":12344,"content":12345,"nodeType":1921},{},[12346],{"data":12347,"marks":12348,"value":2031,"nodeType":1907},{},[],{"data":12350,"content":12351,"nodeType":12356},{},[12352],{"data":12353,"marks":12354,"value":12355,"nodeType":1907},{},[],"Prefill Is Eating The Economics Of Enterprise AI","heading-4",{"data":12358,"content":12359,"nodeType":1921},{},[12360],{"data":12361,"marks":12362,"value":12363,"nodeType":1907},{},[],"Speaking of enterprise scale, as you can imagine, this becomes a much larger problem when prefill starts dominating the economics of inference.",{"data":12365,"content":12366,"nodeType":1921},{},[12367],{"data":12368,"marks":12369,"value":12370,"nodeType":1907},{},[],"In long-context systems, the model might only generate a few hundred output tokens while spending enormous amounts of compute ingesting documents, processing token relationships, and building KV cache beforehand. To put this in perspective, one query can make the system process hundreds of thousands (even millions) of tokens just to prepare the context window needed for reasoning, hence all we’re hearing about token sprawl and teams hitting their token limits way faster than expected.",{"data":12372,"content":12373,"nodeType":1921},{},[12374],{"data":12375,"marks":12376,"value":12377,"nodeType":1907},{},[],"All of this changes the cost structure of enterprise AI. The expensive part is no longer just generation speed or model size (though those things do matter too). Increasingly, it’s actually the repeated construction of context itself.",{"data":12379,"content":12380,"nodeType":1921},{},[12381],{"data":12382,"marks":12383,"value":12386,"nodeType":1907},{},[12384],{"type":12385},"italic","Multi-document reasoning, coding assistants, compliance review, enterprise search, and agentic workflows all depend on large contextual windows that have to be continuously rebuilt during inference. The larger those windows become, the more GPU resources are consumed during prefill, and the more expensive it becomes to repeatedly reconstruct nearly identical semantic state across users and sessions.",{"data":12388,"content":12389,"nodeType":1921},{},[12390],{"data":12391,"marks":12392,"value":12393,"nodeType":1907},{},[],"All of this waste compounds because the resulting KV cache usually remains trapped inside temporary runtime memory tied to a specific GPU allocation, inference engine, or session. Once the workload moves or the session ends, the context disappears with it, even though the enterprise may need that same understanding again minutes later.",{"data":12395,"content":12396,"nodeType":1921},{},[12397],{"data":12398,"marks":12399,"value":12400,"nodeType":1907},{},[],"What this means is that KV cache is no longer just a model optimization detail buried inside inference engines. At enterprise scale, it directly affects cost, latency, throughput, and GPU utilization, which means discarded context windows are becoming one of the largest hidden inefficiencies in modern AI infrastructure.",{"data":12402,"content":12403,"nodeType":1921},{},[12404],{"data":12405,"marks":12406,"value":2031,"nodeType":1907},{},[],{"data":12408,"content":12409,"nodeType":12356},{},[12410],{"data":12411,"marks":12412,"value":12413,"nodeType":1907},{},[],"KV Cache Is Actually Enterprise Knowledge",{"data":12415,"content":12416,"nodeType":1921},{},[12417],{"data":12418,"marks":12419,"value":12420,"nodeType":1907},{},[],"If prefill is becoming one of the most expensive parts of enterprise AI, then the obvious question is why the resulting context is still treated as disposable.",{"data":12422,"content":12423,"nodeType":1921},{},[12424],{"data":12425,"marks":12426,"value":12427,"nodeType":1907},{},[],"Today, most infrastructure treats KV cache as temporary inference memory. The system builds it during runtime, keeps it briefly inside GPU or host memory, then throws it away when the session ends. But at enterprise scale, that cache increasingly represents expensive semantic work the organization already paid to create.",{"data":12429,"content":12430,"nodeType":1921},{},[12431],{"data":12432,"marks":12433,"value":12434,"nodeType":1907},{},[],"A support agent querying a product manual isn’t just retrieving documents, the system is building contextual understanding around those documents. And in that example of the coding assistant loading a repository is constructing awareness of files, dependencies, APIs, and prior changes. Financial analysts too querying the same filings and reports are repeatedly rebuilding nearly identical contextual understanding across users and sessions.",{"data":12436,"content":12437,"nodeType":1921},{},[12438],{"data":12439,"marks":12440,"value":12442,"nodeType":1907},{},[12441],{"type":12385},"There is no real reason that contextual understanding has to remain temporary other than the fact that inference systems were originally designed that way. Once KV cache is treated as reusable data instead of short-lived memory, the architecture starts to change.",{"data":12444,"content":12445,"nodeType":1921},{},[12446],{"data":12447,"marks":12448,"value":12449,"nodeType":1907},{},[],"Context can persist across sessions. Similar requests can reuse already-prepared understanding instead of rebuilding it from scratch. Requests can be routed toward systems where relevant context already exists. Over time, the enterprise begins building a persistent layer of reusable understanding alongside documents, embeddings, and metadata.",{"data":12451,"content":12452,"nodeType":1921},{},[12453],{"data":12454,"marks":12455,"value":12456,"nodeType":1907},{},[],"The system stops asking “what information is relevant?” and starts asking “what understanding already exists?”",{"data":12458,"content":12459,"nodeType":1921},{},[12460],{"data":12461,"marks":12462,"value":2031,"nodeType":1907},{},[],{"data":12464,"content":12465,"nodeType":12356},{},[12466],{"data":12467,"marks":12468,"value":12469,"nodeType":1907},{},[],"The Infrastructure Stack Is Built For The Wrong Model",{"data":12471,"content":12472,"nodeType":1921},{},[12473],{"data":12474,"marks":12475,"value":12476,"nodeType":1907},{},[],"Once contextual understanding becomes persistent and reusable, the traditional AI stack starts looking a little…fragmented.",{"data":12478,"content":12479,"nodeType":1921},{},[12480],{"data":12481,"marks":12482,"value":12483,"nodeType":1907},{},[],"Most enterprise AI infra was built on the assumption that storage, retrieval, inference, and orchestration were separate problems. Documents live in one system, embeddings in another, vector indexes somewhere else, GPU infra separate from storage and so on with orchestration layers trying to coordinate everything from above. That all made sense when inference was mostly short-lived and contextual memory disappeared as soon as a session ended.",{"data":12485,"content":12486,"nodeType":1921},{},[12487],{"data":12488,"marks":12489,"value":12490,"nodeType":1907},{},[],"Now, reusable KV cache changes those assumptions because context itself starts behaving like enterprise data (needs to be stored, shared, routed, replicated, indexed, protected, and reused across users, agents, and workloads). That means the infrastructure can no longer treat inference memory as something isolated inside a GPU server because the contextual understanding generated during inference now has lasting operational value.",{"data":12492,"content":12493,"nodeType":1921},{},[12494],{"data":12495,"marks":12496,"value":12497,"nodeType":1907},{},[],"As that happens, the boundaries between storage, retrieval, inference, and orchestration start breaking down too. So, a query engine might need awareness of where relevant context already exists before assigning workloads for instance or storage systems might need to manage persistent KV alongside source documents and embeddings. Orchestration layers too might need to route requests not just based on available GPU capacity, but based on where useful context is already warm and accessible.",{"data":12499,"content":12500,"nodeType":1921},{},[12501],{"data":12502,"marks":12503,"value":12504,"nodeType":1907},{},[],"The result is infrastructure that behaves less like a chain of disconnected AI services, which always should have been the goal. And when you zoom out you see it’s more like a unified platform where data, inference, and contextual understanding operate together as part of the same system.",{"data":12506,"content":12507,"nodeType":1921},{},[12508],{"data":12509,"marks":12510,"value":2031,"nodeType":1907},{},[],{"data":12512,"content":12513,"nodeType":12356},{},[12514],{"data":12515,"marks":12516,"value":12517,"nodeType":1907},{},[],"Enterprise AI Is Becoming A Continuous System",{"data":12519,"content":12520,"nodeType":1921},{},[12521,12525,12533],{"data":12522,"marks":12523,"value":12524,"nodeType":1907},{},[],"That new kind of data-driven platform can go way beyond just retrieving data and serving inference requests and can get ",{"data":12526,"content":12528,"nodeType":1916},{"uri":12527},"..\u002F..\u002Fblog\u002Fnvidia-dynamo-vast-scalable-optimized-inference",[12529],{"data":12530,"marks":12531,"value":12532,"nodeType":1907},{},[],"enterprises closer to the AI efficiency grail",{"data":12534,"marks":12535,"value":12536,"nodeType":1907},{},[]," of gradually building a shared layer of contextual understanding that sits alongside documents, embeddings, metadata, and broader workflows.",{"data":12538,"content":12539,"nodeType":1921},{},[12540],{"data":12541,"marks":12542,"value":12543,"nodeType":1907},{},[],"This means requests can move toward existing context, agents can inherit understanding generated earlier by other users or systems and the enterprise can begin building an environment where useful semantic work compounds instead of disappearing after each session.",{"data":12545,"content":12546,"nodeType":1921},{},[12547,12551,12559],{"data":12548,"marks":12549,"value":12550,"nodeType":1907},{},[],"More broadly speaking, at that point, the distinction between storage infrastructure, inference infrastructure, and orchestration infrastructure becomes much harder to separate cleanly because all of them are participating in the continuous management of contextual understanding across the environment. 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and multi-turn AI conversations create significant amounts of intermediate key-value data that is used by inference applications. Because GPU memory is a precious resource, these results cannot be kept forever in GPU memory, thus creating a memory wall where not all intermediate data can be kept in GPU memory. ","list-item",{"data":12831,"content":12832,"nodeType":12829},{},[12833],{"data":12834,"content":12835,"nodeType":1921},{},[12836],{"data":12837,"marks":12838,"value":12839,"nodeType":1907},{},[],"The NVIDIA Dynamo inference framework is bringing novel capabilities to market such as key-value cache (KV$ in short) offload",{"data":12841,"content":12842,"nodeType":12829},{},[12843],{"data":12844,"content":12845,"nodeType":1921},{},[12846],{"data":12847,"marks":12848,"value":12849,"nodeType":1907},{},[],"KV$ offload enables the moving of GPU memory states from GPU memory to lower-tiers of memory or storage, enabling GPUs to pre-load past conversations or cache frequent prompts, avoiding the expensive recompute operation (aka: prefill) normally required to load large context into GPU memory",{"data":12851,"content":12852,"nodeType":12829},{},[12853],{"data":12854,"content":12855,"nodeType":1921},{},[12856],{"data":12857,"marks":12858,"value":12859,"nodeType":1907},{},[],"The KV$ offload upside is measured in terms of accelerated time-to-first-token (TTFT) and the savings that come from not needing GPU cycles for prefill, thanks to the data already being cached",{"data":12861,"content":12862,"nodeType":12829},{},[12863],{"data":12864,"content":12865,"nodeType":1921},{},[12866],{"data":12867,"marks":12868,"value":12869,"nodeType":1907},{},[],"Both NVIDIA Dynamo and the VAST AI Operating System are designed to move data at the speed of light (or the speed of the network in this case…)",{"data":12871,"content":12872,"nodeType":12829},{},[12873],{"data":12874,"content":12875,"nodeType":1921},{},[12876],{"data":12877,"marks":12878,"value":12879,"nodeType":1907},{},[],"This blog post covers sizing and architecture considerations when building for large-scale inference infrastructure that is enabled for KV$ offload",{"data":12881,"content":12882,"nodeType":12829},{},[12883],{"data":12884,"content":12885,"nodeType":1921},{},[12886,12890,12898,12902,12908,12912,12917],{"data":12887,"marks":12888,"value":12889,"nodeType":1907},{},[],"VAST collaborated with CoreWeave to evaluate ",{"data":12891,"content":12893,"nodeType":1916},{"uri":12892},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fhow-to-reduce-kv-cache-bottlenecks-with-nvidia-dynamo\u002F",[12894],{"data":12895,"marks":12896,"value":12897,"nodeType":1907},{},[],"NVIDIA’s Dynamo KV$",{"data":12899,"marks":12900,"value":12901,"nodeType":1907},{},[]," offload with the VAST AI OS and saw ",{"data":12903,"marks":12904,"value":12907,"nodeType":1907},{},[12905],{"type":12906},"bold","GPU efficiency improve by 90%",{"data":12909,"marks":12910,"value":12911,"nodeType":1907},{},[]," and ",{"data":12913,"marks":12914,"value":12916,"nodeType":1907},{},[12915],{"type":12906},"TTFT improve by 20x",{"data":12918,"marks":12919,"value":12920,"nodeType":1907},{},[]," as compared to when GPUs need to prefill calculate every past conversation",{"data":12922,"content":12923,"nodeType":12829},{},[12924],{"data":12925,"content":12926,"nodeType":1921},{},[12927],{"data":12928,"marks":12929,"value":12930,"nodeType":1907},{},[],"Dynamo data persistence now relies on data batching for large asynchronous writes, and bulk synchronous reads - resulting in an IO profile focused on storage throughput, not IOPS and low latency",{"data":12932,"content":12933,"nodeType":12829},{},[12934],{"data":12935,"content":12936,"nodeType":1921},{},[12937,12941,12949],{"data":12938,"marks":12939,"value":12940,"nodeType":1907},{},[],"Ultimately, VAST’s testing identified that most KV$ offload operations are not storage-limited, but rather they are network-limited. For additional performance improvements, the NVIDIA Spectrum-X Ethernet fabric accelerates AI Storage fabrics by removing performance bottlenecks common to off the shelf (OTS) Ethernet solutions. VAST has integrated with NVIDIA Spectrum-X - more details ",{"data":12942,"content":12944,"nodeType":1916},{"uri":12943},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnvidia-spectrum-x-and-vast-data-platform",[12945],{"data":12946,"marks":12947,"value":12948,"nodeType":1907},{},[],"here",{"data":12950,"marks":12951,"value":1920,"nodeType":1907},{},[],"unordered-list",{"data":12954,"content":12955,"nodeType":12956},{},[],"hr",{"data":12958,"content":12959,"nodeType":1921},{},[12960],{"data":12961,"marks":12962,"value":12963,"nodeType":1907},{},[],"For the past few months, the VAST and NVIDIA engineering teams have been collaborating on improving NVIDIA NIXL and NVIDIA Dynamo. The following analysis reflects the software enhancements we've worked on that focus on aggregating and accessing KV data in large sequentially-accessed blocks - using new approaches to data storage and access such as KVBM block queuing, transfer list management and support for multi-file layouts. ",{"data":12965,"content":12966,"nodeType":1921},{},[12967,12971,12979],{"data":12968,"marks":12969,"value":12970,"nodeType":1907},{},[],"Previously, we wrote about ",{"data":12972,"content":12974,"nodeType":1916},{"uri":12973},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Faccelerating-inference",[12975],{"data":12976,"marks":12977,"value":12978,"nodeType":1907},{},[],"accelerating inference and the AI data pipeline",{"data":12980,"marks":12981,"value":12982,"nodeType":1907},{},[]," where we introduced the critical challenge of KV cache (KV$) management for LLMs. We surveyed the landscape of offloading solutions, including LMCache, vLLM, and NVIDIA Dynamo KVBM, and shared compelling results from our own experiments. Those tests, using NIXL and NVIDIA GPUDirect Storage (GDS) and vLLM\u002FLMCache integration, demonstrated the massive potential of offloading to KV$ VAST, showing a 10× improvement in prefill time for large-context scenarios.",{"data":12984,"content":12985,"nodeType":1921},{},[12986,12990,12996,12999,13007,13011,13019],{"data":12987,"marks":12988,"value":12989,"nodeType":1907},{},[],"Today, we're excited to share the next major step in this journey: the collaboration and integration between the ",{"data":12991,"content":12992,"nodeType":1916},{"uri":11592},[12993],{"data":12994,"marks":12995,"value":1123,"nodeType":1907},{},[],{"data":12997,"marks":12998,"value":12911,"nodeType":1907},{},[],{"data":13000,"content":13002,"nodeType":1916},{"uri":13001},"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fdynamo\u002F",[13003],{"data":13004,"marks":13005,"value":13006,"nodeType":1907},{},[],"NVIDIA Dynamo",{"data":13008,"marks":13009,"value":13010,"nodeType":1907},{},[],". This article will detail Dynamo's sophisticated ",{"data":13012,"content":13014,"nodeType":1916},{"uri":13013},"https:\u002F\u002Fdocs.nvidia.com\u002Fdynamo\u002Farchive\u002F0.2.0\u002Farchitecture\u002Fkv_cache_manager.html",[13015],{"data":13016,"marks":13017,"value":13018,"nodeType":1907},{},[],"architecture",{"data":13020,"marks":13021,"value":13022,"nodeType":1907},{},[]," and show how the VAST AI OS is now integrated as a foundational, high-performance capacity solution to solve the GPU memory wall.",{"data":13024,"content":13025,"nodeType":12356},{},[13026],{"data":13027,"marks":13028,"value":13029,"nodeType":1907},{},[],"What is NVIDIA Dynamo?",{"data":13031,"content":13032,"nodeType":1921},{},[13033,13037,13044],{"data":13034,"marks":13035,"value":13036,"nodeType":1907},{},[],"Before we dive into the problem, let's briefly introduce ",{"data":13038,"content":13040,"nodeType":1916},{"uri":13039},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fwhy-everyones-talking-about-nvidia-dynamo-and-why-it-actually-matters",[13041],{"data":13042,"marks":13043,"value":13006,"nodeType":1907},{},[],{"data":13045,"marks":13046,"value":13047,"nodeType":1907},{},[],". 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As a conversation or document gets longer, the cache size grows, consuming precious high-bandwidth memory (HBM) on the GPU. This creates a hard limit, forcing a difficult choice between supporting long context for a few users or short context for many. Expensive HBM becomes the bottleneck for scaling inference.",{"data":13102,"content":13103,"nodeType":12356},{},[13104],{"data":13105,"marks":13106,"value":13107,"nodeType":1907},{},[],"The Solution: A New Caching Architecture",{"data":13109,"content":13110,"nodeType":1921},{},[13111],{"data":13112,"marks":13113,"value":13114,"nodeType":1907},{},[],"NVIDIA Dynamo's KV Block Manager (KVBM) provides an elegant solution to this problem by creating a sophisticated, multi-tiered system for intelligent memory management. This architecture is designed to place data in the right tier at the right time, balancing speed, capacity, and cost. Understanding the complete vision is key to seeing how VAST fits in. ",{"data":13116,"content":13117,"nodeType":1921},{},[13118],{"data":13119,"marks":13120,"value":13121,"nodeType":1907},{},[],"Here is the four-tiered architecture as defined by NVIDIA:",{"data":13123,"content":13124,"nodeType":12952},{},[13125,13140,13155,13170],{"data":13126,"content":13127,"nodeType":12829},{},[13128],{"data":13129,"content":13130,"nodeType":1921},{},[13131,13136],{"data":13132,"marks":13133,"value":13135,"nodeType":1907},{},[13134],{"type":12906},"G1: Device Memory (GPU HBM):",{"data":13137,"marks":13138,"value":13139,"nodeType":1907},{},[]," The “hot” tier. This is the fastest and most expensive memory, residing directly on the GPU. It holds the cache for tokens being actively computed.",{"data":13141,"content":13142,"nodeType":12829},{},[13143],{"data":13144,"content":13145,"nodeType":1921},{},[13146,13151],{"data":13147,"marks":13148,"value":13150,"nodeType":1907},{},[13149],{"type":12906},"G2: CPU Memory:",{"data":13152,"marks":13153,"value":13154,"nodeType":1907},{},[]," The “warm” staging tier. This is a larger, but slower, tier using the server's main CPU memory. When HBM is needed for new requests, less-active KV$ blocks are moved here.",{"data":13156,"content":13157,"nodeType":12829},{},[13158],{"data":13159,"content":13160,"nodeType":1921},{},[13161,13166],{"data":13162,"marks":13163,"value":13165,"nodeType":1907},{},[13164],{"type":12906},"G3: Local\u002FPooled SSDs (High-Capacity Cache):",{"data":13167,"marks":13168,"value":13169,"nodeType":1907},{},[]," The “cold” tier. This is where the VAST AI OS currently integrates. It is designed for high-performance, massive-scale offloading when the upper tiers are full. VAST provides a shared, scale-out pool of capacity that many GPU nodes can access. In its current implementation, this G3 tier is ephemeral; it serves as a powerful temporary storage layer to support extremely long context windows for the duration of the service's uptime.",{"data":13171,"content":13172,"nodeType":12829},{},[13173],{"data":13174,"content":13175,"nodeType":1921},{},[13176,13181],{"data":13177,"marks":13178,"value":13180,"nodeType":1907},{},[13179],{"type":12906},"G4: Shared Network Storage:",{"data":13182,"marks":13183,"value":13184,"nodeType":1907},{},[]," This final tier is long-term persistence. This tier allows sessions to be saved permanently and then reloaded hours or days later, even after a full GPU system restart. Persistence is a key difference from the G3 tier, which focuses on extending capacity for live inference sessions, especially multi-turn agentic conversations. In the future, the VAST AI OS will also be integrated at the G4 tier to provide \"long-term memory\" capability.",{"data":13186,"content":13187,"nodeType":1921},{},[13188],{"data":13189,"marks":13190,"value":13191,"nodeType":1907},{},[],"This tiered design allows Dynamo to handle memory pressure gracefully. By integrating into the G3 high-capacity cache, the VAST AI OS provides the critical, scalable capacity needed to make use of massive context windows a reality in production inference environments today.",{"data":13193,"content":13213,"nodeType":13214},{"target":13194},{"metadata":13195,"sys":13198,"fields":13205},{"tags":13196,"concepts":13197},[],[],{"space":13199,"id":13201,"type":934,"createdAt":13202,"updatedAt":13202,"environment":13203,"publishedVersion":847,"revision":1136,"locale":673},{"sys":13200},{"type":657,"linkType":658,"id":659},"3rd6P1QQmnIepmPq1YkZd1","2026-01-14T12:20:42.908Z",{"sys":13204},{"id":665,"type":657,"linkType":666},{"title":12769,"description":12769,"file":13206},{"url":13207,"details":13208,"fileName":13212,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3rd6P1QQmnIepmPq1YkZd1\u002F5d441ebfa62d8cefe45cd048c47ddeb1\u002Fnvidia-dynamo-vast-scalable-optimized-inference-1-v2.webp",{"size":13209,"image":13210},46764,{"width":1144,"height":13211},781,"nvidia-dynamo-vast-scalable-optimized-inference-1-v2.webp",[],"embedded-asset-block",{"data":13216,"content":13217,"nodeType":1921},{},[13218,13223,13230],{"data":13219,"marks":13220,"value":13222,"nodeType":1907},{},[13221],{"type":12385},"Dynamo architecture ",{"data":13224,"content":13225,"nodeType":1916},{"uri":13013},[13226],{"data":13227,"marks":13228,"value":13013,"nodeType":1907},{},[13229],{"type":12385},{"data":13231,"marks":13232,"value":2031,"nodeType":1907},{},[],{"data":13234,"content":13235,"nodeType":12956},{},[],{"data":13237,"content":13238,"nodeType":12356},{},[13239],{"data":13240,"marks":13241,"value":13242,"nodeType":1907},{},[],"Architecting For KV$ Offload",{"data":13244,"content":13245,"nodeType":12815},{},[13246],{"data":13247,"marks":13248,"value":13249,"nodeType":1907},{},[],"General Architecture Considerations",{"data":13251,"content":13252,"nodeType":13293},{},[13253,13263,13273,13283],{"data":13254,"content":13255,"nodeType":12829},{},[13256],{"data":13257,"content":13258,"nodeType":1921},{},[13259],{"data":13260,"marks":13261,"value":13262,"nodeType":1907},{},[],"KV$ is stored in large blocks, making KV$ offload a workload that demands high throughput",{"data":13264,"content":13265,"nodeType":12829},{},[13266],{"data":13267,"content":13268,"nodeType":1921},{},[13269],{"data":13270,"marks":13271,"value":13272,"nodeType":1907},{},[],"The network and supporting storage subsystems must scale with\u002Fto the count of GPUs",{"data":13274,"content":13275,"nodeType":12829},{},[13276],{"data":13277,"content":13278,"nodeType":1921},{},[13279],{"data":13280,"marks":13281,"value":13282,"nodeType":1907},{},[],"Each host’s network access to storage will determine its TTFT performance",{"data":13284,"content":13285,"nodeType":12829},{},[13286],{"data":13287,"content":13288,"nodeType":1921},{},[13289],{"data":13290,"marks":13291,"value":13292,"nodeType":1907},{},[],"KV$ carries conversation information and should be encrypted using an encryption key per-customer, following the best practices of securing user information","ordered-list",{"data":13295,"content":13296,"nodeType":12815},{},[13297],{"data":13298,"marks":13299,"value":13300,"nodeType":1907},{},[],"VAST Data Architecture Considerations",{"data":13302,"content":13303,"nodeType":13293},{},[13304,13325,13347],{"data":13305,"content":13306,"nodeType":12829},{},[13307],{"data":13308,"content":13309,"nodeType":1921},{},[13310,13314,13322],{"data":13311,"marks":13312,"value":13313,"nodeType":1907},{},[],"Any VAST cluster supports KV$ offload today via Dynamo’s transfer library (",{"data":13315,"content":13317,"nodeType":1916},{"uri":13316},"https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fnixl",[13318],{"data":13319,"marks":13320,"value":13321,"nodeType":1907},{},[],"NIXL",{"data":13323,"marks":13324,"value":13067,"nodeType":1907},{},[],{"data":13326,"content":13327,"nodeType":12829},{},[13328],{"data":13329,"content":13330,"nodeType":1921},{},[13331,13335,13343],{"data":13332,"marks":13333,"value":13334,"nodeType":1907},{},[],"Use the ",{"data":13336,"content":13338,"nodeType":1916},{"uri":13337},"https:\u002F\u002Fvastnfs.vastdata.com\u002Fdocs\u002F4.5\u002FIntro.html",[13339],{"data":13340,"marks":13341,"value":13342,"nodeType":1907},{},[],"VAST NFS",{"data":13344,"marks":13345,"value":13346,"nodeType":1907},{},[]," driver, to enable multipathing to saturate the IO needs of any GPU machine",{"data":13348,"content":13349,"nodeType":12829},{},[13350],{"data":13351,"content":13352,"nodeType":1921},{},[13353],{"data":13354,"marks":13355,"value":13356,"nodeType":1907},{},[],"While VAST systems can saturate any client’s I\u002FO needs, even on a lossy network, when using NFS-over-TCP, VAST recommends mounting VAST clusters over RoCE (RDMA over converged Ethernet) for optimizing latency, host CPU utilization, and unlocking GPUDirect Storage for direct KV$ retrieval from storage into GPU memory.",{"data":13358,"content":13359,"nodeType":12956},{},[],{"data":13361,"content":13362,"nodeType":12356},{},[13363],{"data":13364,"marks":13365,"value":13366,"nodeType":1907},{},[],"Calculating KV$ Footprint",{"data":13368,"content":13369,"nodeType":1921},{},[13370],{"data":13371,"marks":13372,"value":13373,"nodeType":1907},{},[],"KV$ creates gigabytes of data per prompt and is strongly dependent on model parameters:",{"data":13375,"content":13376,"nodeType":1921},{},[13377],{"data":13378,"marks":13379,"value":13380,"nodeType":1907},{},[],"Let’s take Llama 3.1-405B as an example for determining the size of the KV$ for a single token using standard parameters (126 layers, 8 KV heads, 128 head dimension, and FP16 storage):",{"data":13382,"content":13383,"nodeType":1921},{},[13384],{"data":13385,"marks":13386,"value":13387,"nodeType":1907},{},[],"Size per Token = 126 * 2 ( for Keys and Values)  * 8 * 128 * 2 (bytes) = 516,096 bytes",{"data":13389,"content":13390,"nodeType":1921},{},[13391,13395,13400],{"data":13392,"marks":13393,"value":13394,"nodeType":1907},{},[],"This results in approximately ",{"data":13396,"marks":13397,"value":13399,"nodeType":1907},{},[13398],{"type":12906},"504 KiB per token",{"data":13401,"marks":13402,"value":13403,"nodeType":1907},{},[],". For a single user utilizing a full 127,188-token context window, the cache becomes massive:",{"data":13405,"content":13406,"nodeType":1921},{},[13407,13411,13416],{"data":13408,"marks":13409,"value":13410,"nodeType":1907},{},[],"504KiB\u002Ftoken * 127,188 tokens ≈",{"data":13412,"marks":13413,"value":13415,"nodeType":1907},{},[13414],{"type":12906}," ",{"data":13417,"marks":13418,"value":13419,"nodeType":1907},{},[]," 61 GB",{"data":13421,"content":13422,"nodeType":12356},{},[13423],{"data":13424,"marks":13425,"value":13427,"nodeType":1907},{},[13426],{"type":12906},"Modeling the \"Data Dump\" Scenarios",{"data":13429,"content":13430,"nodeType":1921},{},[13431],{"data":13432,"marks":13433,"value":13434,"nodeType":1907},{},[],"While it helps to have CPU memory and local SSDs to store data into, the large amounts of KV$ data created per each user session begs the need for scalable low cost storage infrastructure. The purpose of the G4 tier is to support massive contexts and serve them back with low latency to multi-turn inference sessions running on multiple GPU nodes. KV$ retrievals spawn low-TTFT, high-throughput read events rather than a gentle trickle of data. \n\nConsider these scenarios:",{"data":13436,"content":13437,"nodeType":12952},{},[13438,13453],{"data":13439,"content":13440,"nodeType":12829},{},[13441],{"data":13442,"content":13443,"nodeType":1921},{},[13444,13449],{"data":13445,"marks":13446,"value":13448,"nodeType":1907},{},[13447],{"type":12906},"The 'Power User' Returns:",{"data":13450,"marks":13451,"value":13452,"nodeType":1907},{},[]," A user accumulates 128,000 tokens worth of context (~61 GB) and pauses. Their session is evicted to VAST to free up memory space on a host to service other users. Upon the return of the original user, the system must rehydrate 61GB of KV$ back into a machine’s GPU memory. This is a 100% cache hit event.",{"data":13454,"content":13455,"nodeType":12829},{},[13456],{"data":13457,"content":13458,"nodeType":1921},{},[13459,13464],{"data":13460,"marks":13461,"value":13463,"nodeType":1907},{},[13462],{"type":12906},"The RAG Workflow:",{"data":13465,"marks":13466,"value":13467,"nodeType":1907},{},[]," An analyst uploads 64,000 tokens worth of documents (~30 GB). Every subsequent question triggers a reload of that 30GB base cache, resulting in >90% cache hits per prompt. Queries which reference the same document set but come from a different analyst will also enjoy a 90% cache hit per prompt.",{"data":13469,"content":13470,"nodeType":12956},{},[],{"data":13472,"content":13473,"nodeType":12815},{},[13474],{"data":13475,"marks":13476,"value":13477,"nodeType":1907},{},[],"How Much Capacity Do You Need?",{"data":13479,"content":13480,"nodeType":1921},{},[13481],{"data":13482,"marks":13483,"value":13484,"nodeType":1907},{},[],"The following is an example formula that can guide customers in sizing for KV$:",{"data":13486,"content":13487,"nodeType":1921},{},[13488,13493],{"data":13489,"marks":13490,"value":13492,"nodeType":1907},{},[13491],{"type":12906},"Total Capacity = ",{"data":13494,"marks":13495,"value":13496,"nodeType":1907},{},[],"Number of Users * Average KV$ per User * Retention Multiplier",{"data":13498,"content":13499,"nodeType":1921},{},[13500],{"data":13501,"marks":13502,"value":13503,"nodeType":1907},{},[],"The retention multiplier is the number of past conversations that are stored for a user.  Let's use our Llama 3.1 405B example, and plan for 100,000 concurrent users or agents:",{"data":13505,"content":13506,"nodeType":12952},{},[13507,13531,13546],{"data":13508,"content":13509,"nodeType":12829},{},[13510],{"data":13511,"content":13512,"nodeType":1921},{},[13513,13518,13522,13527],{"data":13514,"marks":13515,"value":13517,"nodeType":1907},{},[13516],{"type":12906},"Average KV$ per Prompt:",{"data":13519,"marks":13520,"value":13521,"nodeType":1907},{},[]," As large context windows (e.g., 128,000 tokens) are becoming standard for tasks like document analysis or long-running conversations, the cache size per user balloons. A 128k-token context is roughly 61 GB in size. Let's assume a more typical ",{"data":13523,"marks":13524,"value":13526,"nodeType":1907},{},[13525],{"type":12385},"average",{"data":13528,"marks":13529,"value":13530,"nodeType":1907},{},[]," for a prompt is 64K tokens, roughly 30 GB",{"data":13532,"content":13533,"nodeType":12829},{},[13534],{"data":13535,"content":13536,"nodeType":1921},{},[13537,13542],{"data":13538,"marks":13539,"value":13541,"nodeType":1907},{},[13540],{"type":12906},"Retention Multiplier:",{"data":13543,"marks":13544,"value":13545,"nodeType":1907},{},[]," We'll use 15 to provide a good user experience",{"data":13547,"content":13548,"nodeType":12829},{},[13549],{"data":13550,"content":13551,"nodeType":1921},{},[13552,13557,13561],{"data":13553,"marks":13554,"value":13556,"nodeType":1907},{},[13555],{"type":12906},"Calculation:",{"data":13558,"marks":13559,"value":13560,"nodeType":1907},{},[]," 100,000 users * 30 GB * 15 = 45,000,000 GB = ",{"data":13562,"marks":13563,"value":13565,"nodeType":1907},{},[13564],{"type":12906},"45 PB",{"data":13567,"content":13568,"nodeType":1921},{},[13569],{"data":13570,"marks":13571,"value":13572,"nodeType":1907},{},[],"To support 100,000 users\u002Fagents on a large-context model with a robust conversation history, you need 45 petabytes of storage. ",{"data":13574,"content":13575,"nodeType":12956},{},[],{"data":13577,"content":13578,"nodeType":12356},{},[13579],{"data":13580,"marks":13581,"value":13582,"nodeType":1907},{},[],"Understanding the KV$ I\u002FO Profile",{"data":13584,"content":13585,"nodeType":1921},{},[13586,13591],{"data":13587,"marks":13588,"value":13590,"nodeType":1907},{},[13589],{"type":12906},"Asynchronous Writes (Eviction):",{"data":13592,"marks":13593,"value":13594,"nodeType":1907},{},[]," When the GPU HBM (G1) and CPU Memory (G2) tiers fill up, the KVBM evicts less-active cache blocks to the storage tier. This operation is asynchronous; it does not happen in the immediate, real-time path of generating the next token. This means the system can intelligently batch these writes into larger, more efficient operations. There is no strict, real-time latency or high-IOPS requirement for this eviction process.",{"data":13596,"content":13597,"nodeType":1921},{},[13598],{"data":13599,"marks":13600,"value":13601,"nodeType":1907},{},[],"The offload block size granularity is controlled on the Dynamo framework level. Currently, it mirrors the inference engine block size, usually 16 or 32 tokens, but can be enhanced to increase to whatever IO size we want. Upon eviction, the KVBM writes the all of the model layers of the block size, which amounts to:",{"data":13603,"content":13604,"nodeType":1921},{},[13605,13610],{"data":13606,"marks":13607,"value":13609,"nodeType":1907},{},[13608],{"type":12906},"Total Cache per Token (all 126 layers):",{"data":13611,"marks":13612,"value":13613,"nodeType":1907},{},[]," 504 KiB",{"data":13615,"content":13616,"nodeType":1921},{},[13617,13622],{"data":13618,"marks":13619,"value":13621,"nodeType":1907},{},[13620],{"type":12906},"Total Cache per Block (16 tokens):",{"data":13623,"marks":13624,"value":13625,"nodeType":1907},{},[]," 504 KiB\u002Ftoken * 16 tokens ≈ 8 MB",{"data":13627,"content":13628,"nodeType":1921},{},[13629,13634],{"data":13630,"marks":13631,"value":13633,"nodeType":1907},{},[13632],{"type":12906},"Data Written per GPU per Evicted Block (8 ways):",{"data":13635,"marks":13636,"value":13637,"nodeType":1907},{},[]," 8064 KiB \u002F 8 GPUs ≈ 1 MB ",{"data":13639,"content":13640,"nodeType":1921},{},[13641,13646,13650,13655,13659,13664],{"data":13642,"marks":13643,"value":13645,"nodeType":1907},{},[13644],{"type":12906},"Latency-Critical Reads (Retrieval)",{"data":13647,"marks":13648,"value":13649,"nodeType":1907},{},[],": When a user session\u002Fconversation is reactivated, the system must fetch the offloaded KV$. While modern inference engines utilize pipeline parallelism and batching to keep the GPU compute units active with ",{"data":13651,"marks":13652,"value":13654,"nodeType":1907},{},[13653],{"type":12385},"other",{"data":13656,"marks":13657,"value":13658,"nodeType":1907},{},[]," concurrent requests, the TTFT for the ",{"data":13660,"marks":13661,"value":13663,"nodeType":1907},{},[13662],{"type":12385},"specific",{"data":13665,"marks":13666,"value":13667,"nodeType":1907},{},[]," re-activated user\u002Fconversation is entirely bound by how fast this data can be retrieved.",{"data":13669,"content":13670,"nodeType":1921},{},[13671],{"data":13672,"marks":13673,"value":13674,"nodeType":1907},{},[],"Therefore, the goal is not to maximize IOPS (processing many small requests) and overly focus on latency, but to maximize throughput (delivering a massive stream of data instantly). AI inference systems need to stream GBs of data linearly into HBM to unblock that specific sequence. This is a bandwidth-intensive workload where the storage tier must deliver the entire KV$ at line-rate to minimize the response time perceived by the user.",{"data":13676,"content":13677,"nodeType":1921},{},[13678],{"data":13679,"marks":13680,"value":13682,"nodeType":1907},{},[13681],{"type":12385},"NOTE: At larger batch sizes (inference on multiple prompts at once for improved token serving efficiency - less power per token), KV$ load becomes an asynchronous operation, as the next batch is prepared in memory while the existing batch is running.",{"data":13684,"content":13685,"nodeType":12956},{},[],{"data":13687,"content":13688,"nodeType":12356},{},[13689],{"data":13690,"marks":13691,"value":13692,"nodeType":1907},{},[],"Real-World Performance: VAST and Dynamo in Action",{"data":13694,"content":13695,"nodeType":1921},{},[13696],{"data":13697,"marks":13698,"value":13699,"nodeType":1907},{},[],"Hardware:",{"data":13701,"content":13702,"nodeType":12952},{},[13703,13718,13733,13748,13763,13778,13793,13808],{"data":13704,"content":13705,"nodeType":12829},{},[13706],{"data":13707,"content":13708,"nodeType":1921},{},[13709,13714],{"data":13710,"marks":13711,"value":13713,"nodeType":1907},{},[13712],{"type":12906},"Client system:",{"data":13715,"marks":13716,"value":13717,"nodeType":1907},{},[]," NVIDIA HGX™ H100",{"data":13719,"content":13720,"nodeType":12829},{},[13721],{"data":13722,"content":13723,"nodeType":1921},{},[13724,13729],{"data":13725,"marks":13726,"value":13728,"nodeType":1907},{},[13727],{"type":12906},"GPUs:",{"data":13730,"marks":13731,"value":13732,"nodeType":1907},{},[]," 8x NVIDIA HGX H100 80GB HBM3",{"data":13734,"content":13735,"nodeType":12829},{},[13736],{"data":13737,"content":13738,"nodeType":1921},{},[13739,13744],{"data":13740,"marks":13741,"value":13743,"nodeType":1907},{},[13742],{"type":12906},"Network link",{"data":13745,"marks":13746,"value":13747,"nodeType":1907},{},[],": 2x100Gbit\u002Fsec Ethernet, Bluefied-3 integrated connectX",{"data":13749,"content":13750,"nodeType":12829},{},[13751],{"data":13752,"content":13753,"nodeType":1921},{},[13754,13759],{"data":13755,"marks":13756,"value":13758,"nodeType":1907},{},[13757],{"type":12906},"Software:",{"data":13760,"marks":13761,"value":13762,"nodeType":1907},{},[]," NVIDIA Dynamo pre-release 0.7.0 (Nov 21, 2025, modified) + vLLM v0.11.0 (modified) + NIXL 0.7.1",{"data":13764,"content":13765,"nodeType":12829},{},[13766],{"data":13767,"content":13768,"nodeType":1921},{},[13769,13774],{"data":13770,"marks":13771,"value":13773,"nodeType":1907},{},[13772],{"type":12906},"Model:",{"data":13775,"marks":13776,"value":13777,"nodeType":1907},{},[]," clowman\u002FLlama-3.1-405B-Instruct-AWQ-Int4",{"data":13779,"content":13780,"nodeType":12829},{},[13781],{"data":13782,"content":13783,"nodeType":1921},{},[13784,13789],{"data":13785,"marks":13786,"value":13788,"nodeType":1907},{},[13787],{"type":12906},"Run mode:",{"data":13790,"marks":13791,"value":13792,"nodeType":1907},{},[]," Tensor parallelism across 8 GPUs",{"data":13794,"content":13795,"nodeType":12829},{},[13796],{"data":13797,"content":13798,"nodeType":1921},{},[13799,13804],{"data":13800,"marks":13801,"value":13803,"nodeType":1907},{},[13802],{"type":12906},"Storage:",{"data":13805,"marks":13806,"value":13807,"nodeType":1907},{},[]," NFS (TCP, VAST NFS multipath, VAST server)",{"data":13809,"content":13810,"nodeType":12829},{},[13811],{"data":13812,"content":13813,"nodeType":1921},{},[13814,13819],{"data":13815,"marks":13816,"value":13818,"nodeType":1907},{},[13817],{"type":12906},"Prompt:",{"data":13820,"marks":13821,"value":13822,"nodeType":1907},{},[]," 127,188 tokens",{"data":13824,"content":13825,"nodeType":12815},{},[13826],{"data":13827,"marks":13828,"value":13829,"nodeType":1907},{},[],"Results",{"data":13831,"content":13832,"nodeType":1921},{},[13833,13837,13844,13848,13853,13857,13862],{"data":13834,"marks":13835,"value":13836,"nodeType":1907},{},[],"Collaborating on this configuration with our partner ",{"data":13838,"content":13840,"nodeType":1916},{"uri":13839},"https:\u002F\u002Fwww.vastdata.com\u002Fcustomers\u002Fcoreweave",[13841],{"data":13842,"marks":13843,"value":8535,"nodeType":1907},{},[],{"data":13845,"marks":13846,"value":13847,"nodeType":1907},{},[],", VAST consistently saturates the network during KV$ retrieval, sustaining 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link. ",{"data":13867,"content":13886,"nodeType":13214},{"target":13868},{"metadata":13869,"sys":13872,"fields":13879},{"tags":13870,"concepts":13871},[],[],{"space":13873,"id":13875,"type":934,"createdAt":13876,"updatedAt":13876,"environment":13877,"publishedVersion":847,"revision":1136,"locale":673},{"sys":13874},{"type":657,"linkType":658,"id":659},"6DWr3VdK0ZzHJfYC0Wh4a6","2025-12-16T20:19:33.551Z",{"sys":13878},{"id":665,"type":657,"linkType":666},{"title":12769,"description":12769,"file":13880},{"url":13881,"details":13882,"fileName":13885,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6DWr3VdK0ZzHJfYC0Wh4a6\u002Ff41d8b35caefaba50dcc1245b279fb39\u002Fnvidia-dynamo-vast-scalable-optimized-inference-2.webp",{"size":13883,"image":13884},120100,{"width":1144,"height":1145},"nvidia-dynamo-vast-scalable-optimized-inference-2.webp",[],{"data":13888,"content":13889,"nodeType":1921},{},[13890,13895],{"data":13891,"marks":13892,"value":13894,"nodeType":1907},{},[13893],{"type":12906},"Note:",{"data":13896,"marks":13897,"value":13898,"nodeType":1907},{},[]," 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NVIDIA BlueField DPUs provide the CPU cycles to host all aspects of VAST software - colocating placement decisions, access enforcement, and fast-path metadata resolution directly onto the GPU host and eliminating a whole fleet of x86 servers. Now, I\u002FO processing happens close to execution, where inference actually runs.",[],{},{"nodeType":13214,"data":14517,"content":14537},{"target":14518},{"metadata":14519,"sys":14522,"fields":14529},{"tags":14520,"concepts":14521},[],[],{"space":14523,"id":14525,"type":934,"createdAt":14526,"updatedAt":14526,"environment":14527,"publishedVersion":692,"revision":1136,"locale":673},{"sys":14524},{"type":657,"linkType":658,"id":659},"5b0HAep7m2ctGBHAWiJ7Ls","2026-01-06T11:35:01.093Z",{"sys":14528},{"id":665,"type":657,"linkType":666},{"title":14236,"description":14236,"file":14530},{"url":14531,"details":14532,"fileName":14536,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5b0HAep7m2ctGBHAWiJ7Ls\u002F41cf913eba901439a089093ff8a54beb\u002Fmore-inference-less-infrastructure-vast-nvidia-3.webp",{"size":14533,"image":14534},40530,{"width":1144,"height":14535},618,"more-inference-less-infrastructure-vast-nvidia-3.webp",[],{"nodeType":1921,"data":14539,"content":14540},{},[14541],{"nodeType":1907,"value":14542,"marks":14543,"data":14544},"The result is a solution that not only saves big money on x86 HW costs, it also saves power.",[],{},{"nodeType":13214,"data":14546,"content":14567},{"target":14547},{"metadata":14548,"sys":14551,"fields":14559},{"tags":14549,"concepts":14550},[],[],{"space":14552,"id":14554,"type":934,"createdAt":14555,"updatedAt":14556,"environment":14557,"publishedVersion":1117,"revision":692,"locale":673},{"sys":14553},{"type":657,"linkType":658,"id":659},"31WWlz5mE1NiFffkgo8vEx","2026-01-05T20:36:54.025Z","2026-01-09T21:43:19.857Z",{"sys":14558},{"id":665,"type":657,"linkType":666},{"title":14560,"description":14560,"file":14561},"VAST’s CME reduces KV$ power by 75%",{"url":14562,"details":14563,"fileName":14566,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F31WWlz5mE1NiFffkgo8vEx\u002F04af8f38c44df24c2e95167a7251ba97\u002Fmore-inference-less-infrastructure-vast-nvidia-1.webp",{"size":14564,"image":14565},160336,{"width":1144,"height":1145},"more-inference-less-infrastructure-vast-nvidia-1.webp",[],{"nodeType":1921,"data":14569,"content":14570},{},[14571,14575,14582],{"nodeType":1907,"value":14572,"marks":14573,"data":14574},"Second, by eliminating the x86 server layer, VAST RDMA clients can implement zero-copy I\u002FO operations directly from GPU memory (using ",[],{},{"nodeType":1916,"data":14576,"content":14578},{"uri":14577},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fgpudirect",[14579],{"nodeType":1907,"value":14399,"marks":14580,"data":14581},[],{},{"nodeType":1907,"value":14583,"marks":14584,"data":14585},") all the way to the NVMe SSDs that KV data is stored on and retrieved from. By eliminating a copy operation, we can further reduce time-to-first token.",[],{},{"nodeType":13214,"data":14587,"content":14606},{"target":14588},{"metadata":14589,"sys":14592,"fields":14599},{"tags":14590,"concepts":14591},[],[],{"space":14593,"id":14595,"type":934,"createdAt":14596,"updatedAt":14596,"environment":14597,"publishedVersion":847,"revision":1136,"locale":673},{"sys":14594},{"type":657,"linkType":658,"id":659},"6zFnKHnCYwmkdmIn81c200","2026-01-03T00:16:41.897Z",{"sys":14598},{"id":665,"type":657,"linkType":666},{"title":14236,"description":14236,"file":14600},{"url":14601,"details":14602,"fileName":14605,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6zFnKHnCYwmkdmIn81c200\u002F2c1607adf3b5b251ec25347b33652408\u002Fmore-inference-less-infrastructure-4.webp",{"size":14603,"image":14604},116542,{"width":1144,"height":14425},"more-inference-less-infrastructure-4.webp",[],{"nodeType":1921,"data":14608,"content":14609},{},[14610,14614,14622],{"nodeType":1907,"value":14611,"marks":14612,"data":14613},"How is it possible to embed servers into GPU hosts without adding complication? This is all possible thanks to ",[],{},{"nodeType":1916,"data":14615,"content":14617},{"uri":14616},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fhow-it-works",[14618],{"nodeType":1907,"value":14619,"marks":14620,"data":14621},"VAST’s revolutionary DASE architecture",[],{},{"nodeType":1907,"value":14623,"marks":14624,"data":14625},". DASE enables each CNode server container to operate completely in parallel, imposing zero east-west traffic within a multi-server storage environment. 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Each DPU sees the full storage namespace directly, delivering dedicated bandwidth per host without cross-talk as inference concurrency scales.",[14654],{"type":12385},{},{"nodeType":12815,"data":14657,"content":14658},{},[14659],{"nodeType":1907,"value":14660,"marks":14661,"data":14662},"Performance Without Data Services Compromise",[],{},{"nodeType":1921,"data":14664,"content":14665},{},[14666],{"nodeType":1907,"value":14667,"marks":14668,"data":14669},"Since KV data is easily reproducible, we can provide AI labs the tools needed to both optimize for performance while also making it possible to enable a variety of tools for data management. As AI Inference moves out of its exploratory phase into serious application, some AI labs must build services to meet the requirements of heavily regulated industries. With VAST - they don’t need to choose only for maximum performance and can enable a set of optional data services that help them deliver safe and reproducible AI. We can envision a future collaboration with the NVIDIA Dynamo team where Dynamo APIs direct Inference Context Memory Storage systems to handle different data sets differently as they talk to shared services.",[],{},{"nodeType":13214,"data":14671,"content":14693},{"target":14672},{"metadata":14673,"sys":14676,"fields":14684},{"tags":14674,"concepts":14675},[],[],{"space":14677,"id":14679,"type":934,"createdAt":14680,"updatedAt":14681,"environment":14682,"publishedVersion":1238,"revision":940,"locale":673},{"sys":14678},{"type":657,"linkType":658,"id":659},"4vUBIjuD1YB6Lzj1UzoktA","2026-01-05T20:32:53.599Z","2026-01-06T11:33:17.588Z",{"sys":14683},{"id":665,"type":657,"linkType":666},{"title":14685,"description":14685,"file":14686},"The VAST AI Operating System",{"url":14687,"details":14688,"fileName":14692,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4vUBIjuD1YB6Lzj1UzoktA\u002Fa4a7c4766fbea320872fa65534a57ac2\u002Fmore-inference-less-infrastructure-vast-nvidia-2.webp",{"size":14689,"image":14690},67644,{"width":1144,"height":14691},899,"more-inference-less-infrastructure-vast-nvidia-2.webp",[],{"nodeType":1921,"data":14695,"content":14696},{},[14697],{"nodeType":1907,"value":14698,"marks":14699,"data":14700},"Since VAST’s parallel DASE architecture can add additional CNodes out of band, the result is inference history that remains accessible and globally manageable as inference KV$ concurrency increases, while providing a set of tools to provide background services that do not reintroduce the data management overhead the Inference Context Memory Storage Platform is designed to eliminate.",[],{},{"nodeType":12356,"data":14702,"content":14703},{},[14704],{"nodeType":1907,"value":14705,"marks":14706,"data":14707},"How BlueField-4 enables this shift",[],{},{"nodeType":1921,"data":14709,"content":14710},{},[14711],{"nodeType":1907,"value":14712,"marks":14713,"data":14714},"The Inference Context Memory Storage Platform architecture aligns directly with the NVIDIA BlueField‑4 DPU's operation.",[],{},{"nodeType":1921,"data":14716,"content":14717},{},[14718],{"nodeType":1907,"value":14719,"marks":14720,"data":14721},"DPUs create the opportunity for new storage service deployment paradigms. By adding accelerated processing cores directly onto a network adapter, this advanced data processor serves as the enforcement point for placement, access, and validation directly in the data path. By executing these functions on BlueField-4 at line rate, the host CPU is removed from the critical path as DPUs can now talk directly to NVMe devices in storage enclosures over a shared NVMe-oF fabric. Fewer components participate in each access, fewer transitions are required, and system behavior becomes more predictable under load.",[],{},{"nodeType":12356,"data":14723,"content":14724},{},[14725],{"nodeType":1907,"value":14726,"marks":14727,"data":14728},"How NVIDIA Spectrum-X enables this shift",[],{},{"nodeType":1921,"data":14730,"content":14731},{},[14732,14735,14742],{"nodeType":1907,"value":2031,"marks":14733,"data":14734},[],{},{"nodeType":1916,"data":14736,"content":14737},{"uri":12943},[14738],{"nodeType":1907,"value":14739,"marks":14740,"data":14741},"NVIDIA Spectrum‑X Ethernet ",[],{},{"nodeType":1907,"value":14743,"marks":14744,"data":14745},"fabric plays a critical role by delivering low-latency, high-bandwidth transport with congestion control and performance isolation for large AI fabrics. The platform is designed to operate on the Spectrum-X Ethernet fabric as part of the broader Dynamo inference orchestration framework and enable predictable KV$ service under sustained cluster-wide load.",[],{},{"nodeType":12356,"data":14747,"content":14748},{},[14749],{"nodeType":1907,"value":14750,"marks":14751,"data":14752},"Flexibility across deployment models",[],{},{"nodeType":1921,"data":14754,"content":14755},{},[14756],{"nodeType":1907,"value":14757,"marks":14758,"data":14759},"This behavior is not tied to a single hardware form factor.",[],{},{"nodeType":1921,"data":14761,"content":14762},{},[14763],{"nodeType":1907,"value":14764,"marks":14765,"data":14766},"While early deployments may showcase dense and fault-tolerant NVMe enclosures for efficiency and footprint, the NVIDIA Inference Context Memory Storage Platform + VAST architectural principles extend naturally to other configurations built from commodity storage servers. Because inference history is shared across the system and access is enforced close to the GPU, the loss of any individual server does not trigger a system-wide context rebuild in the case where customers choose to disable erasure encoding. Inference continues using the remaining distributed history, avoiding the large-scale recreation events that traditionally follow node failures.",[],{},{"nodeType":12356,"data":14768,"content":14769},{},[14770],{"nodeType":1907,"value":14771,"marks":14772,"data":14773},"Functional outcomes that matter",[],{},{"nodeType":1921,"data":14775,"content":14776},{},[14777],{"nodeType":1907,"value":14778,"marks":14779,"data":14780},"The impact of this architectural shift is straightforward.",[],{},{"nodeType":1921,"data":14782,"content":14783},{},[14784],{"nodeType":1907,"value":14785,"marks":14786,"data":14787},"Inference history becomes accessible as a shared system resource without forcing every access through a centralized and limited storage server processing tier. GPU utilization improves because access decisions and context retrieval happen closer to execution. Concurrency scales because each GPU machine has its own dedicated and parallel data processing services. Power efficiency and infrastructure footprint improve because the whole solution has been fully optimized for end-to-end KV cache serving.",[],{},{"nodeType":1921,"data":14789,"content":14790},{},[14791],{"nodeType":1907,"value":14792,"marks":14793,"data":14794},"These outcomes align directly with the NVIDIA Inference Context Memory Storage Platform's focus on sustained throughput and power efficiency at scale.",[],{},{"nodeType":12356,"data":14796,"content":14797},{},[14798],{"nodeType":1907,"value":14799,"marks":14800,"data":14801},"A leap in coherent inference",[],{},{"nodeType":1921,"data":14803,"content":14804},{},[14805,14809,14817],{"nodeType":1907,"value":14806,"marks":14807,"data":14808},"The platform defines context history as a first-class system memory resource. NVIDIA ",[],{},{"nodeType":1916,"data":14810,"content":14812},{"uri":14811},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fadvancing-the-state-of-the-art-with-nvidia-blue-field-4",[14813],{"nodeType":1907,"value":14814,"marks":14815,"data":14816},"BlueField-4",[],{},{"nodeType":1907,"value":14818,"marks":14819,"data":14820}," provides an efficient storage controller and the enforcement point for placement, access, and isolation at the boundary where inference runs. Spectrum-X Ethernet provides predictable transport. Dynamo provides orchestration.",[],{},{"nodeType":1921,"data":14822,"content":14823},{},[14824],{"nodeType":1907,"value":14825,"marks":14826,"data":14827},"VAST delivers the software core that allows these components to operate as a unified and coherent system, keeping inference history accessible, governable, and efficient as context sizes, concurrency and regulatory requirements continue to grow.",[],{},{"nodeType":1921,"data":14829,"content":14830},{},[14831,14836,14845],{"nodeType":1907,"value":14832,"marks":14833,"data":14835},"Want to see how this all works in practice? Join VAST and NVIDIA for the ",[14834],{"type":12385},{},{"nodeType":1916,"data":14837,"content":14839},{"uri":14838},"https:\u002F\u002Fwww.vastdata.com\u002Fevents\u002Fbreaking-the-inference-bottleneck-how-nvidia-kv-cache-and-vast-unify",[14840],{"nodeType":1907,"value":14841,"marks":14842,"data":14844},"“Breaking the Inference Bottleneck: How NVIDIA KV Cache and VAST Unify Performance, Scale, and Efficiency for the AI Era” ",[14843],{"type":12385},{},{"nodeType":1907,"value":14846,"marks":14847,"data":14849},"webinar to learn how their joint architecture dramatically accelerates AI inference — cutting time-to-first-token, boosting throughput, and reducing GPU cost at scale.",[14848],{"type":12385},{},{"nodeType":1921,"data":14851,"content":14852},{},[14853,14857,14863,14868,14876],{"nodeType":1907,"value":14058,"marks":14854,"data":14856},[14855],{"type":12385},{},{"nodeType":1916,"data":14858,"content":14859},{"uri":9685},[14860],{"nodeType":1907,"value":14067,"marks":14861,"data":14862},[],{},{"nodeType":1907,"value":14864,"marks":14865,"data":14867},", our inaugural user conference, February 24–26, 2026 in Salt Lake City, Utah. Engage with VAST leadership, customers, and partners through deep technical sessions, hands-on labs, and certification programs. \n",[14866],{"type":12385},{},{"nodeType":1916,"data":14869,"content":14871},{"uri":14870},"..\u002F..\u002Fresources\u002Fvast-forward-sessions ",[14872],{"nodeType":1907,"value":14873,"marks":14874,"data":14875},"Experience VAST Forward 2026 On 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data for AI workloads can be complex, but combining the VAST CSI driver and NVIDIA Run:AI’s data source abstraction makes the process dramatically simpler. ",[],{},{"nodeType":1921,"data":15071,"content":15072},{},[15073,15077,15087],{"nodeType":1907,"value":15074,"marks":15075,"data":15076},"Together, ",[],{},{"nodeType":1916,"data":15078,"content":15080},{"uri":15079},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnvidia-run-ai-and-vast",[15081],{"nodeType":1907,"value":15082,"marks":15083,"data":15086},"VAST and NVIDIA Run.ai",[15084],{"type":15085},"underline",{},{"nodeType":1907,"value":15088,"marks":15089,"data":15090}," streamline provisioning, unify storage access, and remove low-level Kubernetes friction through seamless integration with VAST’s multi-protocol architecture and NVIDIA Run.ai’s PVC-based data management. ",[],{},{"nodeType":12356,"data":15092,"content":15093},{},[15094],{"nodeType":1907,"value":15095,"marks":15096,"data":15097},"Simplified Provisioning and Abstraction",[],{},{"nodeType":1921,"data":15099,"content":15100},{},[15101],{"nodeType":1907,"value":15102,"marks":15103,"data":15104},"To see how this integration improves real-world operations, let’s start with how VAST and NVIDIA Run:AI streamline the basic task of provisioning and accessing data.",[],{},{"nodeType":1921,"data":15106,"content":15107},{},[15108,15113],{"nodeType":1907,"value":15109,"marks":15110,"data":15112},"VAST Multi-Protocol Efficiency:",[15111],{"type":12906},{},{"nodeType":1907,"value":15114,"marks":15115,"data":15116}," The VAST CSI driver can provision both block volumes and file system (NFS) volumes (for shared access and model serving) from the same underlying data platform. 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Using VAST CSI, the administrator simply selects the appropriate storage class from the list and then provisions a new or existing PVC based on that selection.",[],{},{"nodeType":13214,"data":15118,"content":15138},{"target":15119},{"metadata":15120,"sys":15123,"fields":15131},{"tags":15121,"concepts":15122},[],[],{"space":15124,"id":15126,"type":934,"createdAt":15127,"updatedAt":15128,"environment":15129,"publishedVersion":712,"revision":901,"locale":673},{"sys":15125},{"type":657,"linkType":658,"id":659},"hLYNpMJCZalHaQRLe7zbb","2025-12-16T13:56:03.037Z","2025-12-17T08:24:19.414Z",{"sys":15130},{"id":665,"type":657,"linkType":666},{"title":15024,"description":15024,"file":15132},{"url":15133,"details":15134,"fileName":15137,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FhLYNpMJCZalHaQRLe7zbb\u002Fd9e8064fe548a4066f12ced7bd876f0f\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-1.webp",{"size":15135,"image":15136},19930,{"width":1144,"height":1145},"simplifying-data-management-with-vast-and-nvidia-run-ai-1.webp",[],{"nodeType":1921,"data":15140,"content":15141},{},[15142],{"nodeType":1907,"value":15143,"marks":15144,"data":15146},"In this example, the storage class for both block and file systems is installed using the VAST CSI.",[15145],{"type":12385},{},{"nodeType":1921,"data":15148,"content":15149},{},[15150,15155],{"nodeType":1907,"value":15151,"marks":15152,"data":15154},"NVIDIA Run:AI PVC Abstraction:",[15153],{"type":12906},{},{"nodeType":1907,"value":15156,"marks":15157,"data":15158}," NVIDIA Run:AI abstracts the complexity of Kubernetes PVCs via its Data Sources feature. An administrator pre-defines PVC type Data Sources (backed by VAST StorageClasses) and exposes them to researchers. Researcher Self-Service: Researchers request data access simply by name (e.g., “project-a-pvc”) rather than needing to know the complex details of the VAST provisioner, StorageClass mount parameters, or volume binding.",[],{},{"nodeType":12356,"data":15160,"content":15161},{},[15162],{"nodeType":1907,"value":15163,"marks":15164,"data":15165},"Consistency and Advanced Feature Utilization",[],{},{"nodeType":1921,"data":15167,"content":15168},{},[15169],{"nodeType":1907,"value":15170,"marks":15171,"data":15172},"Beyond simplifying provisioning, the integration also ensures that advanced VAST features are consistently available through Kubernetes without adding operational overhead.",[],{},{"nodeType":12952,"data":15174,"content":15175},{},[15176,15216],{"nodeType":12829,"data":15177,"content":15178},{},[15179],{"nodeType":1921,"data":15180,"content":15181},{},[15182,15187,15191,15200,15203,15212],{"nodeType":1907,"value":15183,"marks":15184,"data":15186},"Snapshots and Clones:",[15185],{"type":12906},{},{"nodeType":1907,"value":15188,"marks":15189,"data":15190}," VAST’s native storage efficiency and performance features, such as high-performance ",[],{},{"nodeType":1916,"data":15192,"content":15194},{"uri":15193},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbringing-snapshots-to-vasts-element-store",[15195],{"nodeType":1907,"value":15196,"marks":15197,"data":15199},"snapshots",[15198],{"type":15085},{},{"nodeType":1907,"value":12911,"marks":15201,"data":15202},[],{},{"nodeType":1916,"data":15204,"content":15206},{"uri":15205},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fglobal-snapshot-clones-intelligent-multi-site-data-availability",[15207],{"nodeType":1907,"value":15208,"marks":15209,"data":15211},"clones",[15210],{"type":15085},{},{"nodeType":1907,"value":15213,"marks":15214,"data":15215},", are exposed directly through the CSI interface. The administrator manages these capabilities via standard Kubernetes VolumeSnapshot objects, ensuring fast data iteration and recovery without needing to learn separate VAST management tools for basic operations.",[],{},{"nodeType":12829,"data":15217,"content":15218},{},[15219],{"nodeType":1921,"data":15220,"content":15221},{},[15222,15227],{"nodeType":1907,"value":15223,"marks":15224,"data":15226},"Simplified Model Lifecycle:",[15225],{"type":12906},{},{"nodeType":1907,"value":15228,"marks":15229,"data":15230}," The ability to instantly clone a trained model PVC or restore from a snapshot makes managing the lifecycle of development, training, and deployment seamless and efficient for the administrator.",[],{},{"nodeType":12356,"data":15232,"content":15233},{},[15234],{"nodeType":1907,"value":15235,"marks":15236,"data":15237},"Multi-Tenancy on VAST",[],{},{"nodeType":1921,"data":15239,"content":15240},{},[15241,15245,15254],{"nodeType":1907,"value":15242,"marks":15243,"data":15244},"With provisioning and advanced features handled, the next challenge is ensuring each project or team has secure, isolated access to storage. VAST’s ",[],{},{"nodeType":1916,"data":15246,"content":15248},{"uri":15247},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fvideos\u002Fvast-4-6-multi-tenant-qos-demo",[15249],{"nodeType":1907,"value":15250,"marks":15251,"data":15253},"native multi-tenancy",[15252],{"type":15085},{},{"nodeType":1907,"value":15255,"marks":15256,"data":15257}," makes this simple.",[],{},{"nodeType":1921,"data":15259,"content":15260},{},[15261],{"nodeType":1907,"value":15262,"marks":15263,"data":15264},"The VAST Cluster’s multi-tenancy directly supports the needs of a job orchestrator like NVIDIA Run:AI by providing complete administrative and data isolation. Tenants are defined within VAST as isolated domains, each having its own file system hierarchy, unique security policies and performance controls (QoS). ",[],{},{"nodeType":1921,"data":15266,"content":15267},{},[15268],{"nodeType":1907,"value":15269,"marks":15270,"data":15271},"This structure directly enables NVIDIA Run:AI to create a dedicated VAST tenant for each project or department by mapping their respective network segments (Client IP Ranges) to distinct VIP Pools. This ensures every NVIDIA Run:ai project has segregated, high-performance storage access, preventing cross-project interference, managing consumption via individual QoS such as BW and IOPS limitations, quotas, and guaranteeing secure data isolation across NFS and S3 protocols.",[],{},{"nodeType":1921,"data":15273,"content":15274},{},[15275],{"nodeType":1907,"value":15276,"marks":15277,"data":15278},"First, we configure two tenants, differentiated by vippool, and create two corresponding storage classes, each assigned to one of the tenants.",[],{},{"nodeType":1921,"data":15280,"content":15281},{},[15282],{"nodeType":1907,"value":15283,"marks":15284,"data":15285},"Subsequently, we assign two projects in NVIDIA Run:AI, one for each storage class. 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This configuration ensures that every PVC, NFS mount, or S3 bucket is exclusively owned by and isolated within its respective project.",[15315],{"type":12385},{},{"nodeType":13214,"data":15318,"content":15338},{"target":15319},{"metadata":15320,"sys":15323,"fields":15331},{"tags":15321,"concepts":15322},[],[],{"space":15324,"id":15326,"type":934,"createdAt":15327,"updatedAt":15328,"environment":15329,"publishedVersion":712,"revision":901,"locale":673},{"sys":15325},{"type":657,"linkType":658,"id":659},"kdNFeI1EdTQVWw3l6H2HJ","2025-12-16T14:01:52.688Z","2025-12-17T08:23:52.260Z",{"sys":15330},{"id":665,"type":657,"linkType":666},{"title":15024,"description":15024,"file":15332},{"url":15333,"details":15334,"fileName":15337,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FkdNFeI1EdTQVWw3l6H2HJ\u002F5bc2cbf1b6c6a1c9da29b8fcf30a3269\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-3.webp",{"size":15335,"image":15336},36150,{"width":1144,"height":1145},"simplifying-data-management-with-vast-and-nvidia-run-ai-3.webp",[],{"nodeType":12356,"data":15340,"content":15341},{},[15342],{"nodeType":1907,"value":15343,"marks":15344,"data":15345},"Controlling Tenants’ QoS",[],{},{"nodeType":1921,"data":15347,"content":15348},{},[15349],{"nodeType":1907,"value":15350,"marks":15351,"data":15352},"Once tenants are defined, the next step is ensuring each project gets the right performance. ",[],{},{"nodeType":1921,"data":15354,"content":15355},{},[15356,15359,15368],{"nodeType":1907,"value":2031,"marks":15357,"data":15358},[],{},{"nodeType":1916,"data":15360,"content":15362},{"uri":15361},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fenforcing-io-discipline-dynamic-qos-model-training",[15363],{"nodeType":1907,"value":15364,"marks":15365,"data":15367},"VAST’s QoS policies",[15366],{"type":15085},{},{"nodeType":1907,"value":15369,"marks":15370,"data":15371}," give administrators precise control over bandwidth and IOPS for every tenant or user. The policies provide granular control over storage performance by defining minimum guaranteed and maximum allowed limits for bandwidth and IOPS on read\u002Fwrite operations.",[],{},{"nodeType":1921,"data":15373,"content":15374},{},[15375],{"nodeType":1907,"value":15376,"marks":15377,"data":15378},"Crucially, QoS can be applied granularly either to a specific view or to an individual user within a tenant. This capability is essential for a NVIDIA Run:AI administrator, as they can accurately map the isolated VAST tenant dedicated to each Run:AI project to a specific QoS policy.",[],{},{"nodeType":1921,"data":15380,"content":15381},{},[15382],{"nodeType":1907,"value":15383,"marks":15384,"data":15385},"By assigning these QoS policies, the administrator ensures that every project receives its properly provisioned performance limitations (or guarantees), effectively preventing resource contention and guaranteeing that no single project monopolizes the high-performance storage infrastructure.",[],{},{"nodeType":12356,"data":15387,"content":15388},{},[15389],{"nodeType":1907,"value":15390,"marks":15391,"data":15392},"Multiple Sites \u002F Global Namespace Integration",[],{},{"nodeType":1921,"data":15394,"content":15395},{},[15396,15400,15408],{"nodeType":1907,"value":15397,"marks":15398,"data":15399},"While we’ve concentrated on local PVC workloads with NVIDIA Run:AI, VAST’s global namespace (",[],{},{"nodeType":1916,"data":15401,"content":15403},{"uri":15402},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fdataspace",[15404],{"nodeType":1907,"value":3818,"marks":15405,"data":15407},[15406],{"type":15085},{},{"nodeType":1907,"value":15409,"marks":15410,"data":15411},") is vital for extending resource utilization to remote sites and enabling advanced Run:AI scenarios. Let’s discuss two scenarios: ",[],{},{"nodeType":12952,"data":15413,"content":15414},{},[15415,15425],{"nodeType":12829,"data":15416,"content":15417},{},[15418],{"nodeType":1921,"data":15419,"content":15420},{},[15421],{"nodeType":1907,"value":15422,"marks":15423,"data":15424},"Seamless multi-site burst compute for both training and inference",[],{},{"nodeType":12829,"data":15426,"content":15427},{},[15428],{"nodeType":1921,"data":15429,"content":15430},{},[15431],{"nodeType":1907,"value":15432,"marks":15433,"data":15434},"Resilient LLM Training",[],{},{"nodeType":13214,"data":15436,"content":15456},{"target":15437},{"metadata":15438,"sys":15441,"fields":15448},{"tags":15439,"concepts":15440},[],[],{"space":15442,"id":15444,"type":934,"createdAt":15445,"updatedAt":15445,"environment":15446,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15443},{"type":657,"linkType":658,"id":659},"4VJFjVdk0kDHrqMAd5QX8L","2025-12-17T08:23:40.138Z",{"sys":15447},{"id":665,"type":657,"linkType":666},{"title":15024,"description":15024,"file":15449},{"url":15450,"details":15451,"fileName":15455,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4VJFjVdk0kDHrqMAd5QX8L\u002Fb0e52250dfd10cb5363a5fc25784bb1e\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-4.webp",{"size":15452,"image":15453},117848,{"width":1144,"height":15454},808,"simplifying-data-management-with-vast-and-nvidia-run-ai-4.webp",[],{"nodeType":1921,"data":15458,"content":15459},{},[15460,15465,15471,15476,15482],{"nodeType":1907,"value":15461,"marks":15462,"data":15464},"Run:AI can dynamically burst the job queue to a ",[15463],{"type":12385},{},{"nodeType":1907,"value":15466,"marks":15467,"data":15470},"secondary site (Site B)",[15468,15469],{"type":12385},{"type":12906},{},{"nodeType":1907,"value":15472,"marks":15473,"data":15475}," or a ",[15474],{"type":12385},{},{"nodeType":1907,"value":15477,"marks":15478,"data":15481},"cloud environment",[15479,15480],{"type":12385},{"type":12906},{},{"nodeType":1907,"value":15483,"marks":15484,"data":15486}," where more compute resources are available.",[15485],{"type":12385},{},{"nodeType":12356,"data":15488,"content":15489},{},[15490],{"nodeType":1907,"value":15491,"marks":15492,"data":15493},"Burst Compute for Training and Inference",[],{},{"nodeType":1921,"data":15495,"content":15496},{},[15497,15501,15509],{"nodeType":1907,"value":15498,"marks":15499,"data":15500},"This is a classic use case enabled by the ",[],{},{"nodeType":1916,"data":15502,"content":15504},{"uri":15503},"https:\u002F\u002Fwww.vastdata.com\u002Fresources\u002Fvideos\u002Fglobal-namespace-demo",[15505],{"nodeType":1907,"value":15506,"marks":15507,"data":15508},"global, persistent nature",[],{},{"nodeType":1907,"value":15510,"marks":15511,"data":15512}," of the VAST DataSpace.",[],{},{"nodeType":1921,"data":15514,"content":15515},{},[15516,15521],{"nodeType":1907,"value":15517,"marks":15518,"data":15520},"Scenario:",[15519],{"type":12906},{},{"nodeType":1907,"value":15522,"marks":15523,"data":15524}," You start a large training job on Site A (e.g., on older GPUs or fewer resources), but you acquire new, faster and much more compute resources at Site B.",[],{},{"nodeType":1921,"data":15526,"content":15527},{},[15528],{"nodeType":1907,"value":15529,"marks":15530,"data":15532},"Solution:",[15531],{"type":12906},{},{"nodeType":13293,"data":15534,"content":15535},{},[15536,15546,15556],{"nodeType":12829,"data":15537,"content":15538},{},[15539],{"nodeType":1921,"data":15540,"content":15541},{},[15542],{"nodeType":1907,"value":15543,"marks":15544,"data":15545},"A job on Site A writes its training checkpoints to the VAST file system mounted via NFS. ",[],{},{"nodeType":12829,"data":15547,"content":15548},{},[15549],{"nodeType":1921,"data":15550,"content":15551},{},[15552],{"nodeType":1907,"value":15553,"marks":15554,"data":15555},"The PVC for Site B is linked to the Site B VAST system, making the data instantly accessible at the location with superior compute resources. ",[],{},{"nodeType":12829,"data":15557,"content":15558},{},[15559],{"nodeType":1921,"data":15560,"content":15561},{},[15562],{"nodeType":1907,"value":15563,"marks":15564,"data":15565},"NVIDIA Run:AI orchestrates the shutdown of the training job on Site A and launches a new job on Site B, instructing it to resume training from the latest replicated checkpoint path, immediately utilizing the significantly greater GPU compute resources available there. ",[],{},{"nodeType":13214,"data":15567,"content":15586},{"target":15568},{"metadata":15569,"sys":15572,"fields":15579},{"tags":15570,"concepts":15571},[],[],{"space":15573,"id":15575,"type":934,"createdAt":15576,"updatedAt":15576,"environment":15577,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15574},{"type":657,"linkType":658,"id":659},"68urmPYKyxmQ5RH1ZwDewH","2025-12-17T08:26:53.179Z",{"sys":15578},{"id":665,"type":657,"linkType":666},{"title":15024,"description":15024,"file":15580},{"url":15581,"details":15582,"fileName":15585,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F68urmPYKyxmQ5RH1ZwDewH\u002Fdc8556b00c19ac8043e5539c5a41fe85\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-5.webp",{"size":15583,"image":15584},41180,{"width":1144,"height":1145},"simplifying-data-management-with-vast-and-nvidia-run-ai-5.webp",[],{"nodeType":1921,"data":15588,"content":15589},{},[15590,15595],{"nodeType":1907,"value":15591,"marks":15592,"data":15594},"Benefit:",[15593],{"type":12906},{},{"nodeType":1907,"value":15596,"marks":15597,"data":15598}," The job transitions seamlessly, leveraging the global VAST namespace to access the same data instantly on a site with much higher GPU compute power. This utilization of optimal, high-powered resources eliminates data transfer bottlenecks, leading to dramatically faster time-to-model completion.",[],{},{"nodeType":12356,"data":15600,"content":15601},{},[15602],{"nodeType":1907,"value":15603,"marks":15604,"data":15605},"Resilient LLM Training: Checkpoint and Recover in Seconds",[],{},{"nodeType":1921,"data":15607,"content":15608},{},[15609],{"nodeType":1907,"value":15610,"marks":15611,"data":15612},"The training of cutting-edge AI models, often spanning thousands of GPUs over weeks or even months, carries an unavoidable risk: hardware or network failures. At scale, a single GPU crash can halt an entire synchronous training job, wasting GPU‑hours and delaying training.",[],{},{"nodeType":1921,"data":15614,"content":15615},{},[15616,15620,15628],{"nodeType":1907,"value":15617,"marks":15618,"data":15619},"What if you could build resilience directly into your pipeline so that, when disaster strikes, your training picks up from the last checkpoint in just seconds? In this section, we’ll show how VAST’s high-throughput storage combined with NVIDIA Run:AI’s orchestration layer can be combined for checkpointing, automatic recovery, and zero disruption to LLM training with no manual intervention. We’ll walk through a real example - complete with code you can run - and share a ",[],{},{"nodeType":1916,"data":15621,"content":15623},{"uri":15622},"https:\u002F\u002Fgithub.com\u002FEkinKarabulut\u002Frunai-vast-training-resiliency",[15624],{"nodeType":1907,"value":14036,"marks":15625,"data":15627},[15626],{"type":15085},{},{"nodeType":1907,"value":15629,"marks":15630,"data":15631}," to reproduce it.",[],{},{"nodeType":12815,"data":15633,"content":15634},{},[15635],{"nodeType":1907,"value":15636,"marks":15637,"data":15638},"The High Cost of Unplanned Interruptions",[],{},{"nodeType":1921,"data":15640,"content":15641},{},[15642,15646,15666],{"nodeType":1907,"value":15643,"marks":15644,"data":15645},"Failures during training aren’t reserved for hyperscale clusters - they can strike any environment, from a handful of GPUs to multi-region deployments. ",[],{},{"nodeType":1916,"data":15647,"content":15649},{"uri":15648},"https:\u002F\u002Fai.meta.com\u002Fresearch\u002Fpublications\u002Fthe-llama-3-herd-of-models\u002F",[15650,15655,15661],{"nodeType":1907,"value":15651,"marks":15652,"data":15654},"In Meta’s ",[15653],{"type":15085},{},{"nodeType":1907,"value":15656,"marks":15657,"data":15660},"Llama 3",[15658,15659],{"type":15085},{"type":12385},{},{"nodeType":1907,"value":15662,"marks":15663,"data":15665}," pretraining report",[15664],{"type":15085},{},{"nodeType":1907,"value":15667,"marks":15668,"data":15669},", we get a glimpse into just how common these interruptions are:",[],{},{"nodeType":1921,"data":15671,"content":15672},{},[15673,15678,15684,15689,15695],{"nodeType":1907,"value":15674,"marks":15675,"data":15677},"Over a 54-day window, Meta’s 16,384-GPU cluster recorded ",[15676],{"type":12385},{},{"nodeType":1907,"value":15679,"marks":15680,"data":15683},"466 job interruptions",[15681,15682],{"type":12385},{"type":12906},{},{"nodeType":1907,"value":15685,"marks":15686,"data":15688},", 90% of which were unplanned. GPU hardware alone caused ",[15687],{"type":12385},{},{"nodeType":1907,"value":15690,"marks":15691,"data":15694},"59%",[15692,15693],{"type":12385},{"type":12906},{},{"nodeType":1907,"value":15696,"marks":15697,"data":15699}," of those failures.",[15698],{"type":12385},{},{"nodeType":1921,"data":15701,"content":15702},{},[15703],{"nodeType":1907,"value":15704,"marks":15705,"data":15706},"Even if your environment is 1 percent the size of Meta’s, the likelihood of encountering failure grows proportionally with the number of GPUs. Without checkpointing, a single crash can wipe out hours or days of training progress - a huge waste of compute time and a serious bottleneck for any ML team.",[],{},{"nodeType":12815,"data":15708,"content":15709},{},[15710],{"nodeType":1907,"value":15711,"marks":15712,"data":15713},"Why Checkpointing Matters",[],{},{"nodeType":1921,"data":15715,"content":15716},{},[15717,15720,15729],{"nodeType":1907,"value":2031,"marks":15718,"data":15719},[],{},{"nodeType":1916,"data":15721,"content":15723},{"uri":15722},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fa-checkpoint-on-checkpoints-in-llms",[15724],{"nodeType":1907,"value":15725,"marks":15726,"data":15728},"Checkpointing",[15727],{"type":15085},{},{"nodeType":1907,"value":15730,"marks":15731,"data":15732}," - periodically saving model weights and optimizer state - is more than a safety net. It enables you to recover from interruptions without restarting from zero, select the best‑performing model version based on validation metrics, and reproduce experiments reliably. The bigger the model that you are training, the longer a single epoch can take; losing even a few hours due to a crash costs a lot.",[],{},{"nodeType":1921,"data":15734,"content":15735},{},[15736,15739,15748],{"nodeType":1907,"value":2031,"marks":15737,"data":15738},[],{},{"nodeType":1916,"data":15740,"content":15742},{"uri":15741},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Foptimizing-checkpoint-bandwidth-for-llm-training",[15743],{"nodeType":1907,"value":15744,"marks":15745,"data":15747},"Checkpoint strategies",[15746],{"type":15085},{},{"nodeType":1907,"value":15749,"marks":15750,"data":15751}," vary depending on use case: you might save every few minutes (periodic), only when validation accuracy improves (best‑model), at the end of each epoch or steps (last‑epoch\u002Fsteps), or under custom conditions such as hitting a specific loss threshold. Regardless of the approach, the choice of storage for these checkpoints is equally crucial.",[],{},{"nodeType":12815,"data":15753,"content":15754},{},[15755],{"nodeType":1907,"value":15756,"marks":15757,"data":15758},"The Storage Dilemma",[],{},{"nodeType":1921,"data":15760,"content":15761},{},[15762],{"nodeType":1907,"value":15763,"marks":15764,"data":15765},"Many teams default to cloud object stores or local disks for checkpoint storage. While cloud storage offers geo‑redundancy, its latency can stretch multi‑GB checkpoint writes and reads into minutes. Local disks may deliver lower latency, but they live and die with the node - taking your checkpoints offline if the machine fails.",[],{},{"nodeType":1921,"data":15767,"content":15768},{},[15769,15773,15781,15785,15794],{"nodeType":1907,"value":15770,"marks":15771,"data":15772},"VAST bridges these gaps. VAST’s ",[],{},{"nodeType":1916,"data":15774,"content":15775},{"uri":14616},[15776],{"nodeType":1907,"value":15777,"marks":15778,"data":15780},"disaggregated architecture",[15779],{"type":15085},{},{"nodeType":1907,"value":15782,"marks":15783,"data":15784}," aggregates NVMe drives across an entire cluster into a single global namespace, providing parallel I\u002FO at tens of gigabytes per second plus ",[],{},{"nodeType":1916,"data":15786,"content":15788},{"uri":15787},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes",[15789],{"nodeType":1907,"value":15790,"marks":15791,"data":15793},"built‑in erasure coding",[15792],{"type":15085},{},{"nodeType":1907,"value":15795,"marks":15796,"data":15797}," for data durability.",[],{},{"nodeType":12815,"data":15799,"content":15800},{},[15801],{"nodeType":1907,"value":15802,"marks":15803,"data":15804},"Orchestration with NVIDIA Run:AI",[],{},{"nodeType":1921,"data":15806,"content":15807},{},[15808],{"nodeType":1907,"value":15809,"marks":15810,"data":15811},"On the compute side, NVIDIA Run:AI layers atop Kubernetes to manage GPU and data resources intelligently. It continuously monitors pod and node health, automatically rescheduling failed jobs to healthy GPU pools - even across regions. ",[],{},{"nodeType":1921,"data":15813,"content":15814},{},[15815],{"nodeType":1907,"value":15816,"marks":15817,"data":15818},"Mounting a VAST volume into your NVIDIA Run:AI‑managed pod is straightforward: no changes to your training code are required. Your script reads and writes checkpoints just as before, but now with NVMe‑equivalent performance and cross‑region accessibility.",[],{},{"nodeType":12356,"data":15820,"content":15821},{},[15822],{"nodeType":1907,"value":15823,"marks":15824,"data":15825},"Fast, Cross‑Region Recovery in Action",[],{},{"nodeType":1921,"data":15827,"content":15828},{},[15829],{"nodeType":1907,"value":15830,"marks":15831,"data":15832},"In the step-by-step walkthrough below, we launch a PyTorch training job for a LoRa adapter on Llama 3.1 in site A. Checkpoints are written in every 5 steps to a VAST‑backed volume. ",[],{},{"nodeType":1921,"data":15834,"content":15835},{},[15836],{"nodeType":1907,"value":15837,"marks":15838,"data":15839},"We then simulate a GPU host crash in site-A - Run:AI instantly detects the failure and reschedules the pod to a GPU cluster in site-B. ",[],{},{"nodeType":1921,"data":15841,"content":15842},{},[15843],{"nodeType":1907,"value":15844,"marks":15845,"data":15846},"Since the VAST volume is globally accessible, the job reconnects to the same storage namespace; the latest checkpoint streams at full NVMe speed, and training resumes within seconds. What would normally take minutes of downtime is reduced to under half a minute - without any manual intervention.",[],{},{"nodeType":1921,"data":15848,"content":15849},{},[15850],{"nodeType":1907,"value":15851,"marks":15852,"data":15853},"In our approach, we start by preloading the base model into VAST using a data mover container. This avoids the cold start of downloading the model every time training begins. Once that’s in place, we submit the training job using the same VAST-mounted volume. Checkpoints are written to \u002Fmodel\u002Fcheckpoints, and when a failure is triggered, recovery picks up exactly where it left off.",[],{},{"nodeType":1921,"data":15855,"content":15856},{},[15857],{"nodeType":1907,"value":15858,"marks":15859,"data":15861},"Putting it into Practice: A Step-by-Step Job Submission",[15860],{"type":12906},{},{"nodeType":1921,"data":15863,"content":15864},{},[15865,15869,15876],{"nodeType":1907,"value":15866,"marks":15867,"data":15868},"To show this resilience in action, we’ve built a complete working example that you can reproduce. The full source code, scripts, and container definitions are available in our ",[],{},{"nodeType":1916,"data":15870,"content":15871},{"uri":15622},[15872],{"nodeType":1907,"value":14036,"marks":15873,"data":15875},[15874],{"type":15085},{},{"nodeType":1907,"value":1920,"marks":15877,"data":15878},[],{},{"nodeType":1921,"data":15880,"content":15881},{},[15882],{"nodeType":1907,"value":15883,"marks":15884,"data":15886},"Step 1: Creating a PVC with VAST CSI filesystem",[15885],{"type":12906},{},{"nodeType":1921,"data":15888,"content":15889},{},[15890],{"nodeType":1907,"value":15891,"marks":15892,"data":15893},"Let’s create a PVC with VAST CSI filesystem and mount it to \u002Fmodel. ",[],{},{"nodeType":13214,"data":15895,"content":15914},{"target":15896},{"metadata":15897,"sys":15900,"fields":15907},{"tags":15898,"concepts":15899},[],[],{"space":15901,"id":15903,"type":934,"createdAt":15904,"updatedAt":15904,"environment":15905,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15902},{"type":657,"linkType":658,"id":659},"77QPXz8kJ5suw2HsXf7PIO","2025-12-17T08:28:42.070Z",{"sys":15906},{"id":665,"type":657,"linkType":666},{"title":15024,"description":15024,"file":15908},{"url":15909,"details":15910,"fileName":15913,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F77QPXz8kJ5suw2HsXf7PIO\u002F87bbdebb1642d12787d37b08e1feff92\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-6.webp",{"size":15911,"image":15912},51050,{"width":1144,"height":1145},"simplifying-data-management-with-vast-and-nvidia-run-ai-6.webp",[],{"nodeType":1921,"data":15916,"content":15917},{},[15918],{"nodeType":1907,"value":15919,"marks":15920,"data":15921},"Create a new training workload job with pytorch-one-gpu:",[],{},{"nodeType":13214,"data":15923,"content":15943},{"target":15924},{"metadata":15925,"sys":15928,"fields":15935},{"tags":15926,"concepts":15927},[],[],{"space":15929,"id":15931,"type":934,"createdAt":15932,"updatedAt":15932,"environment":15933,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15930},{"type":657,"linkType":658,"id":659},"41lMeyZPrg1hwFMqvUlEdp","2025-12-17T08:30:29.308Z",{"sys":15934},{"id":665,"type":657,"linkType":666},{"title":15024,"description":15024,"file":15936},{"url":15937,"details":15938,"fileName":15942,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F41lMeyZPrg1hwFMqvUlEdp\u002F8ed7d59c7fd81eff3525165efcfe2e2d\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-7.webp",{"size":15939,"image":15940},12608,{"width":1144,"height":15941},521,"simplifying-data-management-with-vast-and-nvidia-run-ai-7.webp",[],{"nodeType":1921,"data":15945,"content":15946},{},[15947,15951,15960],{"nodeType":1907,"value":15948,"marks":15949,"data":15950},"If the model isn’t already downloaded, start by fetching it - in this example, we’ll download Meta-Llama-3.1-8B-Instruct into \u002Fmodel and create the additional checkpoint directories. You can use the model-loading bash script included ",[],{},{"nodeType":1916,"data":15952,"content":15954},{"uri":15953},"https:\u002F\u002Fgithub.com\u002FEkinKarabulut\u002Frunai-vast-training-resiliency\u002Fblob\u002Fmain\u002Fmodel-loading\u002Fclone.sh",[15955],{"nodeType":1907,"value":15956,"marks":15957,"data":15959},"in this repository",[15958],{"type":15085},{},{"nodeType":1907,"value":15961,"marks":15962,"data":15963}," and run it after SSH-ing into the pod. This script downloads Meta-Llama-3.1-8B-Instruct from Hugging Face Hub and writes it to \u002Fmodel\u002FMeta-Llama-3.1-8B-Instruct. It also creates the \u002Fmodel\u002Fcheckpoints directory with the correct permissions where training checkpoints will be written.",[],{},{"nodeType":1921,"data":15965,"content":15966},{},[15967],{"nodeType":1907,"value":15968,"marks":15969,"data":15970},"After this step completes, your VAST volume contains:",[],{},{"nodeType":12952,"data":15972,"content":15973},{},[15974,15984],{"nodeType":12829,"data":15975,"content":15976},{},[15977],{"nodeType":1921,"data":15978,"content":15979},{},[15980],{"nodeType":1907,"value":15981,"marks":15982,"data":15983},"\u002Fmodel\u002FMeta-Llama-3.1-8B-Instruct - The base model (one-time download)",[],{},{"nodeType":12829,"data":15985,"content":15986},{},[15987],{"nodeType":1921,"data":15988,"content":15989},{},[15990],{"nodeType":1907,"value":15991,"marks":15992,"data":15993},"\u002Fmodel\u002Fcheckpoints\u002F - Empty directory ready for checkpoints",[],{},{"nodeType":1921,"data":15995,"content":15996},{},[15997],{"nodeType":1907,"value":15998,"marks":15999,"data":16001},"Step 2: Creating Training Script ",[16000],{"type":12906},{},{"nodeType":1921,"data":16003,"content":16004},{},[16005],{"nodeType":1907,"value":16006,"marks":16007,"data":16008},"Now let’s look at what makes this training resilient: checkpointing. The heart of this example is training\u002Fdistributed.py, which implements checkpointing and recovery; ",[],{},{"nodeType":12952,"data":16010,"content":16011},{},[16012,16027,16042,16057],{"nodeType":12829,"data":16013,"content":16014},{},[16015],{"nodeType":1921,"data":16016,"content":16017},{},[16018,16023],{"nodeType":1907,"value":16019,"marks":16020,"data":16022},"Checkpoint frequency: ",[16021],{"type":12906},{},{"nodeType":1907,"value":16024,"marks":16025,"data":16026},"Saves every 5 training steps to \u002Fmodel\u002Fcheckpoints\u002FMeta-Llama-3.1-8B-Instruct-finetuned\u002F",[],{},{"nodeType":12829,"data":16028,"content":16029},{},[16030],{"nodeType":1921,"data":16031,"content":16032},{},[16033,16038],{"nodeType":1907,"value":16034,"marks":16035,"data":16037},"Automatic resume logic: ",[16036],{"type":12906},{},{"nodeType":1907,"value":16039,"marks":16040,"data":16041},"On startup, the script searches for existing checkpoints and automatically resumes from the latest one",[],{},{"nodeType":12829,"data":16043,"content":16044},{},[16045],{"nodeType":1921,"data":16046,"content":16047},{},[16048,16053],{"nodeType":1907,"value":16049,"marks":16050,"data":16052},"LoRa fine-tuning:",[16051],{"type":12906},{},{"nodeType":1907,"value":16054,"marks":16055,"data":16056}," Uses parameter-efficient LoRa adapters to fine-tune Llama 3.1 with minimal memory overhead",[],{},{"nodeType":12829,"data":16058,"content":16059},{},[16060],{"nodeType":1921,"data":16061,"content":16062},{},[16063,16068],{"nodeType":1907,"value":16064,"marks":16065,"data":16067},"Distributed training ready:",[16066],{"type":12906},{},{"nodeType":1907,"value":16069,"marks":16070,"data":16071}," Leverages torchrun for multi-GPU training via the launch.sh entrypoint",[],{},{"nodeType":1921,"data":16073,"content":16074},{},[16075],{"nodeType":1907,"value":16076,"marks":16077,"data":16079},"Step 3: Build the Training Image (or Use Ours)",[16078],{"type":12906},{},{"nodeType":1921,"data":16081,"content":16082},{},[16083],{"nodeType":1907,"value":16084,"marks":16085,"data":16086},"The training\u002F directory includes a Dockerfile that packages everything needed. ",[],{},{"nodeType":1921,"data":16088,"content":16089},{},[16090],{"nodeType":1907,"value":16091,"marks":16092,"data":16093},"If you want to build your own image:",[],{},{"nodeType":1921,"data":16095,"content":16096},{},[16097],{"nodeType":1907,"value":16098,"marks":16099,"data":16102},"cd training \ndocker build --platform linux\u002Famd64 -t \u003CYOUR_REGISTRY>\u002Frunai-vast-training\ndocker push \u003CYOUR_REGISTRY>\u002Frunai-vast-training",[16100],{"type":16101},"code",{},{"nodeType":1921,"data":16104,"content":16105},{},[16106,16110,16119],{"nodeType":1907,"value":16107,"marks":16108,"data":16109},"Alternatively, you can use ",[],{},{"nodeType":1916,"data":16111,"content":16113},{"uri":16112},"https:\u002F\u002Fhub.docker.com\u002Fr\u002Fekarabulut844\u002Frunai-vast-training",[16114],{"nodeType":1907,"value":16115,"marks":16116,"data":16118},"our pre-built Docker image",[16117],{"type":15085},{},{"nodeType":1907,"value":16120,"marks":16121,"data":16122}," that’s ready to go. ",[],{},{"nodeType":1921,"data":16124,"content":16125},{},[16126],{"nodeType":1907,"value":16127,"marks":16128,"data":16130},"Step 4: Launch the Training Job",[16129],{"type":12906},{},{"nodeType":1921,"data":16132,"content":16133},{},[16134],{"nodeType":1907,"value":16135,"marks":16136,"data":16137},"With the model loaded on VAST and the training image ready, submit your training job via Run:AI:",[],{},{"nodeType":1921,"data":16139,"content":16140},{},[16141],{"nodeType":1907,"value":16142,"marks":16143,"data":16145},"runai training pytorch submit lora-fine-tune-model --existing-pvc \"claimname=$DATA,path=\u002Fmodel\" -i docker.io\u002F\u003CREGISTRY>\u002Frunai-vast-training -g 1",[16144],{"type":16101},{},{"nodeType":1921,"data":16147,"content":16148},{},[16149],{"nodeType":1907,"value":16150,"marks":16151,"data":16152},"Once the job starts, you’ll see training logs showing the LoRa fine-tuning in progress. Every 5 steps, you’ll see checkpoint confirmations: ",[],{},{"nodeType":13214,"data":16154,"content":16174},{"target":16155},{"metadata":16156,"sys":16159,"fields":16166},{"tags":16157,"concepts":16158},[],[],{"space":16160,"id":16162,"type":934,"createdAt":16163,"updatedAt":16163,"environment":16164,"publishedVersion":847,"revision":1136,"locale":673},{"sys":16161},{"type":657,"linkType":658,"id":659},"qqhA5Y8rP0XN94LDN7MeA","2025-12-17T08:38:12.304Z",{"sys":16165},{"id":665,"type":657,"linkType":666},{"title":15024,"description":15024,"file":16167},{"url":16168,"details":16169,"fileName":16173,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FqqhA5Y8rP0XN94LDN7MeA\u002F5f56d596378a69b3341bfeea28ae4fbe\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-8.webp",{"size":16170,"image":16171},204520,{"width":1144,"height":16172},972,"simplifying-data-management-with-vast-and-nvidia-run-ai-8.webp",[],{"nodeType":1921,"data":16176,"content":16177},{},[16178],{"nodeType":1907,"value":16179,"marks":16180,"data":16181},"Checkpoints are already saving to \u002Fmodel. Now we can simulate a failure on site A, and load, for example, an inference job on site B using checkpoint-40…",[],{},{"nodeType":1921,"data":16183,"content":16184},{},[16185,16190,16196],{"nodeType":1907,"value":16186,"marks":16187,"data":16189},"root@training-0-0:\u002Fmodel# ls -ltr checkpoints\u002F\ntotal Ø\n\ndrwxr-xr-x 2 nobody nogroup 4096 Nov 19 17:21 Meta-Llama-3.1-8B-Instruct-finetuned root@training-0-ø:\u002Fmodel# ls -ltr checkpoints\u002FMeta-Llama-3.1-8B-Instruct-finetuned\u002F\ntotal 2\n\n",[16188],{"type":16101},{},{"nodeType":1907,"value":16191,"marks":16192,"data":16195},"drwxr-xr-x 2 nobody nogroup 4096 Nov 19 17:18 checkpoint-40\n",[16193,16194],{"type":12906},{"type":16101},{},{"nodeType":1907,"value":16197,"marks":16198,"data":16200},"-rw-r-r-1 nobody nogroup 1429 Noy 19 17:21 README.md\ndrwxr-xr-x 2 nobody nogroup 4096 Nov 19 17:21 checkpoint-80",[16199],{"type":16101},{},{"nodeType":1921,"data":16202,"content":16203},{},[16204],{"nodeType":1907,"value":16205,"marks":16206,"data":16207},"… by creating an inference workload with the following arguments:",[],{},{"nodeType":1921,"data":16209,"content":16210},{},[16211],{"nodeType":1907,"value":16212,"marks":16213,"data":16215},"cmd: vllm\narggs: serve \u002Fmodel\u002FMeta-Llama-3.1-8B-Instruct --enable-lora --lora-modules adapter=\u002Fmodel\u002Fcheckpoints\u002FMeta-Llama-3.1-8B-Instruct-finetuned\u002Fcheckpoint-40 --max-model-len 1000 --enforce-eager --max-lora-rank 32",[16214],{"type":16101},{},{"nodeType":13214,"data":16217,"content":16237},{"target":16218},{"metadata":16219,"sys":16222,"fields":16229},{"tags":16220,"concepts":16221},[],[],{"space":16223,"id":16225,"type":934,"createdAt":16226,"updatedAt":16226,"environment":16227,"publishedVersion":847,"revision":1136,"locale":673},{"sys":16224},{"type":657,"linkType":658,"id":659},"38SHUNOFYtJfSLk5DvLWJm","2025-12-17T08:41:04.447Z",{"sys":16228},{"id":665,"type":657,"linkType":666},{"title":15024,"description":15024,"file":16230},{"url":16231,"details":16232,"fileName":16236,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F38SHUNOFYtJfSLk5DvLWJm\u002F145deaae37713e46c143c7bc48b78aba\u002Fsimplifying-data-management-with-vast-and-nvidia-run-ai-9.webp",{"size":16233,"image":16234},18250,{"width":1144,"height":16235},341,"simplifying-data-management-with-vast-and-nvidia-run-ai-9.webp",[],{"nodeType":12356,"data":16239,"content":16240},{},[16241],{"nodeType":1907,"value":16242,"marks":16243,"data":16244},"Final Thoughts",[],{},{"nodeType":1921,"data":16246,"content":16247},{},[16248,16251,16260],{"nodeType":1907,"value":2031,"marks":16249,"data":16250},[],{},{"nodeType":1916,"data":16252,"content":16254},{"uri":16253},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbringing-rack-level-resilience-to-vast-part-one",[16255],{"nodeType":1907,"value":16256,"marks":16257,"data":16259},"Resilience isn’t optional",[16258],{"type":15085},{},{"nodeType":1907,"value":16261,"marks":16262,"data":16263}," when training large models. Between hardware faults, preemptions, and scaling challenges, your pipeline needs to recover gracefully without human intervention. 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Lead",{"metadata":16485,"sys":16488,"fields":16498},{"tags":16486,"concepts":16487},[],[],{"space":16489,"id":16491,"type":14,"createdAt":16492,"updatedAt":16493,"environment":16494,"publishedVersion":692,"revision":901,"contentType":16496,"locale":673},{"sys":16490},{"type":657,"linkType":658,"id":659},"3JBrkiSDpWU2oz7kqGPoAD","2025-11-19T14:16:36.018Z","2025-11-19T14:39:57.064Z",{"sys":16495},{"id":665,"type":657,"linkType":666},{"sys":16497},{"type":657,"linkType":671,"id":12173},{"entryTitle":16480,"image":16499},{"metadata":16500,"sys":16503,"fields":16510},{"tags":16501,"concepts":16502},[],[],{"space":16504,"id":16506,"type":934,"createdAt":16507,"updatedAt":16507,"environment":16508,"publishedVersion":847,"revision":1136,"locale":673},{"sys":16505},{"type":657,"linkType":658,"id":659},"59azC7qOX174KM5JuIGPev","2025-11-19T14:39:52.667Z",{"sys":16509},{"id":665,"type":657,"linkType":666},{"title":16480,"description":16480,"file":16511},{"url":16512,"details":16513,"fileName":16516,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F59azC7qOX174KM5JuIGPev\u002F07a01a232753ec5f643361aa722ac1a8\u002Fbeyond-the-loop-architecting-a-vast-multi-agent-ai-system.webp",{"size":16514,"image":16515},106370,{"width":1144,"height":1145},"beyond-the-loop-architecting-a-vast-multi-agent-ai-system.webp",{"nodeType":1922,"data":16518,"content":16519},{},[16520,16528,16531,16538,16550,16566,16573,16597,16604,16611,16618,16690,16697,16704,16711,16727,16743,16814,16834,16841,16848,16855,16862,16870,16877,16920,16927,16935,16942,16950,16957,17005,17013,17020,17027,17034,17077,17084,17091,17098,17106,17113,17176,17183,17246,17253,17261,17268,17275,17308,17326,17374,17381,17429,17436,17443,17450,17498,17505,17553,17561,17568,17616,17623,17630,17647,17655,17662,17669,17676,17684,17691,17724,17731,17739,17746,17753,17786,17794,17801,17854,17861,17909,17916,17923,17930,17937,17985,17993,18000,18033,18040,18048,18055,18088,18095,18103,18110,18163,18170,18177,18184,18265,18272,18320,18327,18336,18343,18362],{"nodeType":1921,"data":16521,"content":16522},{},[16523],{"nodeType":1907,"value":16524,"marks":16525,"data":16527},"Why the future of AI is a swarm of millions of agents, and how the DASE architecture provides the foundation to scale it.",[16526],{"type":12385},{},{"nodeType":12956,"data":16529,"content":16530},{},[],{"nodeType":1921,"data":16532,"content":16533},{},[16534],{"nodeType":1907,"value":16535,"marks":16536,"data":16537},"We are at a critical inflection point, one that fundamentally reshapes the future for both cloud service providers and enterprise buyers. The industry is rapidly moving beyond “AI as a chat box” and hurtling toward “AI as an autonomous workforce.” Today’s single-agent demos, running in a simple while loop, are the “hello, world” prototypes of a new computing era. ",[],{},{"nodeType":1921,"data":16539,"content":16540},{},[16541,16545],{"nodeType":1907,"value":16542,"marks":16543,"data":16544},"The future isn’t one hyper-intelligent agent; it’s a swarm of millions. Why? ",[],{},{"nodeType":1907,"value":16546,"marks":16547,"data":16549},"Specialization.",[16548],{"type":12906},{},{"nodeType":1921,"data":16551,"content":16552},{},[16553,16557,16562],{"nodeType":1907,"value":16554,"marks":16555,"data":16556},"A single company isn’t one hyper-productive CEO. It’s a team of specialists in finance, engineering, marketing, and logistics, all collaborating. Complex AI problems are no different. You wouldn’t ask one agent to run a global supply chain, design a silicon chip, or manage a live marketing campaign. These tasks require a ",[],{},{"nodeType":1907,"value":16558,"marks":16559,"data":16561},"team of specialized agents",[16560],{"type":12385},{},{"nodeType":1907,"value":16563,"marks":16564,"data":16565}," —a PlannerAgent, a ResearchAgent, a DataAnalystAgent, a SecurityMonitorAgent, and a NotificationAgent — all working in concert.",[],{},{"nodeType":1921,"data":16567,"content":16568},{},[16569],{"nodeType":1907,"value":16570,"marks":16571,"data":16572},"For CSPs, this shift represents the next generation of massive compute demand and a new, high-value AI platform to host. For enterprise buyers, this marks the opportunity to achieve unprecedented levels of automation and operational efficiency.",[],{},{"nodeType":1921,"data":16574,"content":16575},{},[16576,16580,16589,16593],{"nodeType":1907,"value":16577,"marks":16578,"data":16579},"However, this also creates an unprecedented infrastructure problem. A swarm of millions of autonomous agents, all reasoning, communicating, and accessing data simultaneously, ",[],{},{"nodeType":1916,"data":16581,"content":16583},{"uri":16582},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbuilding-enterprise-infrastructure-for-ai-agents",[16584],{"nodeType":1907,"value":16585,"marks":16586,"data":16588},"cannot run on today’s siloed architecture",[16587],{"type":12906},{},{"nodeType":1907,"value":1920,"marks":16590,"data":16592},[16591],{"type":12906},{},{"nodeType":1907,"value":16594,"marks":16595,"data":16596}," It’s an infrastructure nightmare, creating problems of cost, security, and complexity.",[],{},{"nodeType":12356,"data":16598,"content":16599},{},[16600],{"nodeType":1907,"value":16601,"marks":16602,"data":16603},"The Bottleneck: The Environment vs. The Swarm",[],{},{"nodeType":1921,"data":16605,"content":16606},{},[16607],{"nodeType":1907,"value":16608,"marks":16609,"data":16610},"The primary challenge in scaling from a single agent to a swarm is the lack of infrastructure designed for multi-agent orchestration. Today’s agent deployments typically rely on general-purpose infrastructure—virtual machines, containers, or serverless functions—that wasn’t built with swarm coordination in mind. While modern platforms can scale individual components, they lack the integrated primitives agents need to operate collectively at scale.",[],{},{"nodeType":1921,"data":16612,"content":16613},{},[16614],{"nodeType":1907,"value":16615,"marks":16616,"data":16617},"This creates critical bottlenecks that make swarm deployment challenging:",[],{},{"nodeType":12952,"data":16619,"content":16620},{},[16621,16636,16651,16666],{"nodeType":12829,"data":16622,"content":16623},{},[16624],{"nodeType":1921,"data":16625,"content":16626},{},[16627,16632],{"nodeType":1907,"value":16628,"marks":16629,"data":16631},"The coupled scaling problem:",[16630],{"type":12906},{},{"nodeType":1907,"value":16633,"marks":16634,"data":16635}," General-purpose infrastructure doesn’t natively understand agent-specific resource patterns. An agent’s compute needs (LLM inference) and memory needs (vector stores, conversation history) scale differently, but standard deployment models don’t provide first-class primitives for independent scaling of agent-specific resources. You end up either over-provisioning or constantly reconfiguring infrastructure to match workload patterns.",[],{},{"nodeType":12829,"data":16637,"content":16638},{},[16639],{"nodeType":1921,"data":16640,"content":16641},{},[16642,16647],{"nodeType":1907,"value":16643,"marks":16644,"data":16646},"The state and amnesia problem:",[16645],{"type":12906},{},{"nodeType":1907,"value":16648,"marks":16649,"data":16650}," Agents need durable, accessible state across invocations—conversation history, tool outputs, intermediate reasoning. Standard platforms offer generic databases or object storage, but lack agent-native state management that understands semantic context, supports efficient retrieval, handles state synchronization across multiple agents, and survives infrastructure changes. Developers must cobble together solutions from generic components, leading to fragility.",[],{},{"nodeType":12829,"data":16652,"content":16653},{},[16654],{"nodeType":1921,"data":16655,"content":16656},{},[16657,16662],{"nodeType":1907,"value":16658,"marks":16659,"data":16661},"The communication bottleneck:",[16660],{"type":12906},{},{"nodeType":1907,"value":16663,"marks":16664,"data":16665}," Coordinating thousands of agents requires high-throughput, asynchronous, observable communication patterns. While message queues and event brokers exist, they’re not agent-aware—they don’t understand task delegation, result aggregation, or semantic routing. Implementing swarm coordination on generic messaging infrastructure means building the orchestration layer yourself, which is complex and error-prone.",[],{},{"nodeType":12829,"data":16667,"content":16668},{},[16669],{"nodeType":1921,"data":16670,"content":16671},{},[16672,16677,16681,16686],{"nodeType":1907,"value":16673,"marks":16674,"data":16676},"The security and identity silo:",[16675],{"type":12906},{},{"nodeType":1907,"value":16678,"marks":16679,"data":16680}," In a monolithic setup, each agent is an island. It has to manage its ",[],{},{"nodeType":1907,"value":16682,"marks":16683,"data":16685},"own",[16684],{"type":12385},{},{"nodeType":1907,"value":16687,"marks":16688,"data":16689}," secrets and API keys. The platform provides no central, secure identity or tool execution service, creating thousands of individual security risks instead of one manageable one. This lack of centralized management extends to vital infrastructure like Model Context Protocol tools, which also become disparate and difficult to govern across the agent ecosystem.",[],{},{"nodeType":1921,"data":16691,"content":16692},{},[16693],{"nodeType":1907,"value":16694,"marks":16695,"data":16696},"These aren’t agent failures; they’re platform gaps - the absence of infrastructure primitives purpose-built for multi-agent systems.",[],{},{"nodeType":12356,"data":16698,"content":16699},{},[16700],{"nodeType":1907,"value":16701,"marks":16702,"data":16703},"A Shared, Decoupled Foundation for Running AI Agents",[],{},{"nodeType":1921,"data":16705,"content":16706},{},[16707],{"nodeType":1907,"value":16708,"marks":16709,"data":16710},"VAST’s architectural approach to this problem is built on a core design principle: compute and state must be fully separated, but all compute resources must have shared, high-speed access to a global, consistent state layer.",[],{},{"nodeType":1921,"data":16712,"content":16713},{},[16714,16718,16723],{"nodeType":1907,"value":16715,"marks":16716,"data":16717},"It turns out that agentic workloads are a perfect fit for this model. Rather than running agents in an “all-in-one-box” environment, we can run the ",[],{},{"nodeType":1907,"value":16719,"marks":16720,"data":16722},"separate functions",[16721],{"type":12385},{},{"nodeType":1907,"value":16724,"marks":16725,"data":16726}," of an agent - its compute logic, its reasoning, its state, and its communication - as their own layers on our platform. The agent’s logic (CPU) runs on one set of resources, while its state and memory (storage) live on another, accessible by all. CPU compute, GPU compute, storage, and networking can all be scaled independently, which opens the door for efficiently and economically serving large numbers of autonomous agents.",[],{},{"nodeType":1921,"data":16728,"content":16729},{},[16730,16734,16739],{"nodeType":1907,"value":16731,"marks":16732,"data":16733},"The primary advantage of this disaggregated architecture is the ability to scale ",[],{},{"nodeType":1907,"value":16735,"marks":16736,"data":16738},"only",[16737],{"type":12385},{},{"nodeType":1907,"value":16740,"marks":16741,"data":16742}," the component that is the bottleneck.",[],{},{"nodeType":12952,"data":16744,"content":16745},{},[16746,16761,16784,16799],{"nodeType":12829,"data":16747,"content":16748},{},[16749],{"nodeType":1921,"data":16750,"content":16751},{},[16752,16757],{"nodeType":1907,"value":16753,"marks":16754,"data":16756},"Scaling CPU (kernels)",[16755],{"type":12906},{},{"nodeType":1907,"value":16758,"marks":16759,"data":16760},": If 10,000 tasks arrive, the platform spins up 10,000 cheap, stateless Agent Kernels (on VAST C-Nodes).",[],{},{"nodeType":12829,"data":16762,"content":16763},{},[16764],{"nodeType":1921,"data":16765,"content":16766},{},[16767,16772,16776,16780],{"nodeType":1907,"value":16768,"marks":16769,"data":16771},"Scaling GPU (reasoning)",[16770],{"type":12906},{},{"nodeType":1907,"value":16773,"marks":16774,"data":16775},": If those agents create 100,000 reasoning requests, ",[],{},{"nodeType":1907,"value":16735,"marks":16777,"data":16779},[16778],{"type":12385},{},{"nodeType":1907,"value":16781,"marks":16782,"data":16783}," the GPU servers are scaled, backed by the VAST KVCache to maximize GPU utility.",[],{},{"nodeType":12829,"data":16785,"content":16786},{},[16787],{"nodeType":1921,"data":16788,"content":16789},{},[16790,16795],{"nodeType":1907,"value":16791,"marks":16792,"data":16794},"Scaling storage (memory)",[16793],{"type":12906},{},{"nodeType":1907,"value":16796,"marks":16797,"data":16798},": If agents need to access shared knowledge, the VAST AI OS (on VAST D-Nodes) scales its read replicas and internal bandwidth independently of compute.",[],{},{"nodeType":12829,"data":16800,"content":16801},{},[16802],{"nodeType":1921,"data":16803,"content":16804},{},[16805,16810],{"nodeType":1907,"value":16806,"marks":16807,"data":16809},"Scaling I\u002FO (comms)",[16808],{"type":12906},{},{"nodeType":1907,"value":16811,"marks":16812,"data":16813},": If agents pass massive data payloads, the Event Broker and the VAST fabric’s internal network handle the I\u002FO without the compute nodes becoming a bottleneck.",[],{},{"nodeType":13214,"data":16815,"content":16833},{"target":16816},{"metadata":16817,"sys":16820,"fields":16827},{"tags":16818,"concepts":16819},[],[],{"space":16821,"id":16823,"type":934,"createdAt":16824,"updatedAt":16824,"environment":16825,"publishedVersion":847,"revision":1136,"locale":673},{"sys":16822},{"type":657,"linkType":658,"id":659},"1O2NCk6JSuSs1UXnguRpiS","2025-11-19T14:14:44.267Z",{"sys":16826},{"id":665,"type":657,"linkType":666},{"title":16480,"description":16480,"file":16828},{"url":16829,"details":16830,"fileName":16516,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1O2NCk6JSuSs1UXnguRpiS\u002Fa2a722a7966a4271d9fc7ff17f7fa313\u002Fbeyond-the-loop-architecting-a-vast-multi-agent-ai-system.webp",{"size":16831,"image":16832},159002,{"width":1144,"height":1145},[],{"nodeType":12356,"data":16835,"content":16836},{},[16837],{"nodeType":1907,"value":16838,"marks":16839,"data":16840},"Architectural and Engineering Deep Dive: Pillars of a Scalable Agentic Platform",[],{},{"nodeType":1921,"data":16842,"content":16843},{},[16844],{"nodeType":1907,"value":16845,"marks":16846,"data":16847},"This section examines the core components required to run disaggregated agents at scale on the VAST AI OS, addressing the fundamental challenges of coordination, state, scaling, and operability.",[],{},{"nodeType":12815,"data":16849,"content":16850},{},[16851],{"nodeType":1907,"value":16852,"marks":16853,"data":16854},"1. The Execution Model: Stateless Compute with Shared State Access",[],{},{"nodeType":1921,"data":16856,"content":16857},{},[16858],{"nodeType":1907,"value":16859,"marks":16860,"data":16861},"The fundamental architectural pattern is simple: agent logic runs as stateless compute, while all persistent state lives in a globally-accessible storage layer. Applying this pattern to agents requires solving specific problems around latency, consistency, and scale.",[],{},{"nodeType":1921,"data":16863,"content":16864},{},[16865],{"nodeType":1907,"value":16866,"marks":16867,"data":16869},"Agent Kernels as Ephemeral Functions",[16868],{"type":12906},{},{"nodeType":1921,"data":16871,"content":16872},{},[16873],{"nodeType":1907,"value":16874,"marks":16875,"data":16876},"Agent “kernels” (the executable logic of an agent) run as serverless functions on VAST C-Nodes (compute nodes). These functions are stateless and short-lived. They:",[],{},{"nodeType":12952,"data":16878,"content":16879},{},[16880,16890,16900,16910],{"nodeType":12829,"data":16881,"content":16882},{},[16883],{"nodeType":1921,"data":16884,"content":16885},{},[16886],{"nodeType":1907,"value":16887,"marks":16888,"data":16889},"Execute in response to events (e.g., file writes, object creation, table mutations)",[],{},{"nodeType":12829,"data":16891,"content":16892},{},[16893],{"nodeType":1921,"data":16894,"content":16895},{},[16896],{"nodeType":1907,"value":16897,"marks":16898,"data":16899},"Have direct NVMe access to the underlying storage fabric",[],{},{"nodeType":12829,"data":16901,"content":16902},{},[16903],{"nodeType":1921,"data":16904,"content":16905},{},[16906],{"nodeType":1907,"value":16907,"marks":16908,"data":16909},"Scale horizontally without coordination, so spawning 10,000 agent kernels doesn’t require inter-kernel communication",[],{},{"nodeType":12829,"data":16911,"content":16912},{},[16913],{"nodeType":1921,"data":16914,"content":16915},{},[16916],{"nodeType":1907,"value":16917,"marks":16918,"data":16919},"Die after execution; state persistence is the storage layer’s responsibility, not the kernel’s",[],{},{"nodeType":1921,"data":16921,"content":16922},{},[16923],{"nodeType":1907,"value":16924,"marks":16925,"data":16926},"This is a different architectural approach from traditional container-based agents, where the agent’s logic, memory, and state are bundled. Here, the kernel is throwaway; only the storage persists.",[],{},{"nodeType":1921,"data":16928,"content":16929},{},[16930],{"nodeType":1907,"value":16931,"marks":16932,"data":16934},"The Data Locality Advantage",[16933],{"type":12906},{},{"nodeType":1921,"data":16936,"content":16937},{},[16938],{"nodeType":1907,"value":16939,"marks":16940,"data":16941},"Because kernels run on C-Nodes that are part of the storage fabric, data access is local NVMe, not remote network I\u002FO. When an agent needs to read a 10GB dataset, that data doesn’t move across a network—the compute moves to the data. This is critical for agents doing data-intensive work (e.g., a DataAnalysisAgent processing logs or a VectorSearchAgent scanning embeddings).",[],{},{"nodeType":1921,"data":16943,"content":16944},{},[16945],{"nodeType":1907,"value":16946,"marks":16947,"data":16949},"Execution Triggers and Lifecycle",[16948],{"type":12906},{},{"nodeType":1921,"data":16951,"content":16952},{},[16953],{"nodeType":1907,"value":16954,"marks":16955,"data":16956},"Agent kernel triggers can be:",[],{},{"nodeType":13293,"data":16958,"content":16959},{},[16960,16975,16990],{"nodeType":12829,"data":16961,"content":16962},{},[16963],{"nodeType":1921,"data":16964,"content":16965},{},[16966,16971],{"nodeType":1907,"value":16967,"marks":16968,"data":16970},"Event-driven",[16969],{"type":12906},{},{"nodeType":1907,"value":16972,"marks":16973,"data":16974},": File write, object creation, table insert → kernel spawns automatically",[],{},{"nodeType":12829,"data":16976,"content":16977},{},[16978],{"nodeType":1921,"data":16979,"content":16980},{},[16981,16986],{"nodeType":1907,"value":16982,"marks":16983,"data":16985},"API-driven",[16984],{"type":12906},{},{"nodeType":1907,"value":16987,"marks":16988,"data":16989},": External system calls an endpoint → kernel executes on-demand",[],{},{"nodeType":12829,"data":16991,"content":16992},{},[16993],{"nodeType":1921,"data":16994,"content":16995},{},[16996,17001],{"nodeType":1907,"value":16997,"marks":16998,"data":17000},"Scheduled",[16999],{"type":12906},{},{"nodeType":1907,"value":17002,"marks":17003,"data":17004},": Cron-like triggers for periodic agent tasks",[],{},{"nodeType":1921,"data":17006,"content":17007},{},[17008],{"nodeType":1907,"value":17009,"marks":17010,"data":17012},"When is it Important to Write Agents Output Storage:",[17011],{"type":12906},{},{"nodeType":1921,"data":17014,"content":17015},{},[17016],{"nodeType":1907,"value":17017,"marks":17018,"data":17019},"In-memory storage is used for short-lived, task-specific context such as conversational state, temporary results, inter-agent messages, or intermediate computations. This minimizes latency and provides fast, stateful interactions crucial for responsiveness in ongoing agent flows.",[],{},{"nodeType":1921,"data":17021,"content":17022},{},[17023],{"nodeType":1907,"value":17024,"marks":17025,"data":17026},"Persistent storage (databases, object stores, file systems) holds data that must survive agent restarts, session losses, or system failures, such as project histories, decision logs, audit trails, user profiles, and learned strategies or policies. This ensures reliability, traceability, and long-term knowledge accumulation",[],{},{"nodeType":1921,"data":17028,"content":17029},{},[17030],{"nodeType":1907,"value":17031,"marks":17032,"data":17033},"Write everything to persistent storage when:",[],{},{"nodeType":12952,"data":17035,"content":17036},{},[17037,17047,17057,17067],{"nodeType":12829,"data":17038,"content":17039},{},[17040],{"nodeType":1921,"data":17041,"content":17042},{},[17043],{"nodeType":1907,"value":17044,"marks":17045,"data":17046},"Regulatory or compliance requirements mandate traceability (finance, healthcare)",[],{},{"nodeType":12829,"data":17048,"content":17049},{},[17050],{"nodeType":1921,"data":17051,"content":17052},{},[17053],{"nodeType":1907,"value":17054,"marks":17055,"data":17056},"System reliability is paramount",[],{},{"nodeType":12829,"data":17058,"content":17059},{},[17060],{"nodeType":1921,"data":17061,"content":17062},{},[17063],{"nodeType":1907,"value":17064,"marks":17065,"data":17066},"Agents work on long-horizon tasks",[],{},{"nodeType":12829,"data":17068,"content":17069},{},[17070],{"nodeType":1921,"data":17071,"content":17072},{},[17073],{"nodeType":1907,"value":17074,"marks":17075,"data":17076},"Agent interactions should be inspectable, replayable, or learning-enabled for future deployments.",[],{},{"nodeType":12815,"data":17078,"content":17079},{},[17080],{"nodeType":1907,"value":17081,"marks":17082,"data":17083},"2. Event-Driven Coordination: Communication Through the Environment",[],{},{"nodeType":1921,"data":17085,"content":17086},{},[17087],{"nodeType":1907,"value":17088,"marks":17089,"data":17090},"Agents don’t communicate through direct messages alone. They communicate through the environment — by modifying shared state, writing files, updating tables, or inserting vectors. Other agents observe these changes and react.",[],{},{"nodeType":1921,"data":17092,"content":17093},{},[17094],{"nodeType":1907,"value":17095,"marks":17096,"data":17097},"This is a fundamentally different communication model from traditional RPC or message-passing. Instead of AgentA explicitly calling AgentB, AgentA modifies the environment (writes a file, updates a table), and the platform automatically triggers AgentB based on registered event patterns.",[],{},{"nodeType":1921,"data":17099,"content":17100},{},[17101],{"nodeType":1907,"value":17102,"marks":17103,"data":17105},"Environment-Driven Agent Triggers",[17104],{"type":12906},{},{"nodeType":1921,"data":17107,"content":17108},{},[17109],{"nodeType":1907,"value":17110,"marks":17111,"data":17112},"Agents register themselves to be triggered by specific environmental changes:",[],{},{"nodeType":12952,"data":17114,"content":17115},{},[17116,17131,17146,17161],{"nodeType":12829,"data":17117,"content":17118},{},[17119],{"nodeType":1921,"data":17120,"content":17121},{},[17122,17127],{"nodeType":1907,"value":17123,"marks":17124,"data":17126},"File system events",[17125],{"type":12906},{},{"nodeType":1907,"value":17128,"marks":17129,"data":17130},": An agent triggers when a file matching \u002Fvast\u002Fdatasets\u002Fsales_*.parquet is created",[],{},{"nodeType":12829,"data":17132,"content":17133},{},[17134],{"nodeType":1921,"data":17135,"content":17136},{},[17137,17142],{"nodeType":1907,"value":17138,"marks":17139,"data":17141},"Database mutations",[17140],{"type":12906},{},{"nodeType":1907,"value":17143,"marks":17144,"data":17145},": An agent triggers when a new row is inserted into the pending_tasks table",[],{},{"nodeType":12829,"data":17147,"content":17148},{},[17149],{"nodeType":1921,"data":17150,"content":17151},{},[17152,17157],{"nodeType":1907,"value":17153,"marks":17154,"data":17156},"Vector updates",[17155],{"type":12906},{},{"nodeType":1907,"value":17158,"marks":17159,"data":17160},": An agent triggers when new embeddings are added to the knowledge base",[],{},{"nodeType":12829,"data":17162,"content":17163},{},[17164],{"nodeType":1921,"data":17165,"content":17166},{},[17167,17172],{"nodeType":1907,"value":17168,"marks":17169,"data":17171},"Time-based events",[17170],{"type":12906},{},{"nodeType":1907,"value":17173,"marks":17174,"data":17175},": An agent triggers on a schedule to perform periodic analysis",[],{},{"nodeType":1921,"data":17177,"content":17178},{},[17179],{"nodeType":1907,"value":17180,"marks":17181,"data":17182},"Here’s an example workflow:",[],{},{"nodeType":13293,"data":17184,"content":17185},{},[17186,17196,17206,17216,17226,17236],{"nodeType":12829,"data":17187,"content":17188},{},[17189],{"nodeType":1921,"data":17190,"content":17191},{},[17192],{"nodeType":1907,"value":17193,"marks":17194,"data":17195},"DataCollectorAgent writes a new file: \u002Fvast\u002Fdatasets\u002Fsales_2025_q3.parquet",[],{},{"nodeType":12829,"data":17197,"content":17198},{},[17199],{"nodeType":1921,"data":17200,"content":17201},{},[17202],{"nodeType":1907,"value":17203,"marks":17204,"data":17205},"The file write generates a file.created event on the Event Broker",[],{},{"nodeType":12829,"data":17207,"content":17208},{},[17209],{"nodeType":1921,"data":17210,"content":17211},{},[17212],{"nodeType":1907,"value":17213,"marks":17214,"data":17215},"DataValidatorAgent, registered to trigger on file.created events in \u002Fvast\u002Fdatasets\u002F, automatically spawns",[],{},{"nodeType":12829,"data":17217,"content":17218},{},[17219],{"nodeType":1921,"data":17220,"content":17221},{},[17222],{"nodeType":1907,"value":17223,"marks":17224,"data":17225},"DataValidatorAgent reads the file, validates it, and updates a database table with status = “validated”",[],{},{"nodeType":12829,"data":17227,"content":17228},{},[17229],{"nodeType":1921,"data":17230,"content":17231},{},[17232],{"nodeType":1907,"value":17233,"marks":17234,"data":17235},"The table update generates a table.updated event",[],{},{"nodeType":12829,"data":17237,"content":17238},{},[17239],{"nodeType":1921,"data":17240,"content":17241},{},[17242],{"nodeType":1907,"value":17243,"marks":17244,"data":17245},"AnalysisPipelineAgent, registered to trigger on validation status changes, automatically spawns",[],{},{"nodeType":1921,"data":17247,"content":17248},{},[17249],{"nodeType":1907,"value":17250,"marks":17251,"data":17252},"No agent explicitly called another agent. They communicated through changes to the shared environment, with the Event Broker mediating the triggers.",[],{},{"nodeType":1921,"data":17254,"content":17255},{},[17256],{"nodeType":1907,"value":17257,"marks":17258,"data":17260},"Why This Model Scales",[17259],{"type":12906},{},{"nodeType":1921,"data":17262,"content":17263},{},[17264],{"nodeType":1907,"value":17265,"marks":17266,"data":17267},"Direct agent-to-agent messaging requires agents to know about each other. At scale (10,000+ agents), this creates a dependency graph nightmare. When 10,000 agents are operating simultaneously, each producing dozens of state changes per second, you’re looking at hundreds of thousands to millions of events per second. Most event systems collapse under this load.",[],{},{"nodeType":1921,"data":17269,"content":17270},{},[17271],{"nodeType":1907,"value":17272,"marks":17273,"data":17274},"Environment-driven communication decouples agents completely:",[],{},{"nodeType":12952,"data":17276,"content":17277},{},[17278,17288,17298],{"nodeType":12829,"data":17279,"content":17280},{},[17281],{"nodeType":1921,"data":17282,"content":17283},{},[17284],{"nodeType":1907,"value":17285,"marks":17286,"data":17287},"Agents don’t need to know which other agents exist",[],{},{"nodeType":12829,"data":17289,"content":17290},{},[17291],{"nodeType":1921,"data":17292,"content":17293},{},[17294],{"nodeType":1907,"value":17295,"marks":17296,"data":17297},"New agents can be added without modifying existing agents",[],{},{"nodeType":12829,"data":17299,"content":17300},{},[17301],{"nodeType":1921,"data":17302,"content":17303},{},[17304],{"nodeType":1907,"value":17305,"marks":17306,"data":17307},"Failed agents don’t cause cascading failures—their work simply remains in the environment until they recover",[],{},{"nodeType":1921,"data":17309,"content":17310},{},[17311,17314,17322],{"nodeType":1907,"value":2031,"marks":17312,"data":17313},[],{},{"nodeType":1916,"data":17315,"content":17317},{"uri":17316},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fannouncing-the-vast-event-broker",[17318],{"nodeType":1907,"value":17319,"marks":17320,"data":17321},"VAST’s Event Broker",[],{},{"nodeType":1907,"value":17323,"marks":17324,"data":17325}," is architecturally different because it’s not a separate service; it’s integrated directly into the storage fabric. This integration provides scalability and performance characteristics that are critical for multi-agent environments. The broker doesn’t maintain events in separate in-memory buffers or on dedicated broker nodes. Events are written directly to D-Nodes as structured data on the same storage fabric that handles all other I\u002FO. This means:",[],{},{"nodeType":12952,"data":17327,"content":17328},{},[17329,17344,17359],{"nodeType":12829,"data":17330,"content":17331},{},[17332],{"nodeType":1921,"data":17333,"content":17334},{},[17335,17340],{"nodeType":1907,"value":17336,"marks":17337,"data":17339},"No broker bottleneck",[17338],{"type":12906},{},{"nodeType":1907,"value":17341,"marks":17342,"data":17343},": Event capacity scales with storage capacity, not broker node count",[],{},{"nodeType":12829,"data":17345,"content":17346},{},[17347],{"nodeType":1921,"data":17348,"content":17349},{},[17350,17355],{"nodeType":1907,"value":17351,"marks":17352,"data":17354},"Unlimited retention",[17353],{"type":12906},{},{"nodeType":1907,"value":17356,"marks":17357,"data":17358},": Events can persist indefinitely because storage is cheap and abundant",[],{},{"nodeType":12829,"data":17360,"content":17361},{},[17362],{"nodeType":1921,"data":17363,"content":17364},{},[17365,17370],{"nodeType":1907,"value":17366,"marks":17367,"data":17369},"Parallel consumption",[17368],{"type":12906},{},{"nodeType":1907,"value":17371,"marks":17372,"data":17373},": Thousands of agents can consume events concurrently without overwhelming broker nodes—they’re reading directly from distributed storage",[],{},{"nodeType":1921,"data":17375,"content":17376},{},[17377],{"nodeType":1907,"value":17378,"marks":17379,"data":17380},"Because events are part of the storage fabric:",[],{},{"nodeType":12952,"data":17382,"content":17383},{},[17384,17399,17414],{"nodeType":12829,"data":17385,"content":17386},{},[17387],{"nodeType":1921,"data":17388,"content":17389},{},[17390,17395],{"nodeType":1907,"value":17391,"marks":17392,"data":17394},"Sub-millisecond publish latency",[17393],{"type":12906},{},{"nodeType":1907,"value":17396,"marks":17397,"data":17398},": Writing an event is a storage write, leveraging NVMe and RDMA",[],{},{"nodeType":12829,"data":17400,"content":17401},{},[17402],{"nodeType":1921,"data":17403,"content":17404},{},[17405,17410],{"nodeType":1907,"value":17406,"marks":17407,"data":17409},"Massive parallel throughput",[17408],{"type":12906},{},{"nodeType":1907,"value":17411,"marks":17412,"data":17413},": The storage fabric can handle millions of events per second across the cluster",[],{},{"nodeType":12829,"data":17415,"content":17416},{},[17417],{"nodeType":1921,"data":17418,"content":17419},{},[17420,17425],{"nodeType":1907,"value":17421,"marks":17422,"data":17424},"No data movement for triggers",[17423],{"type":12906},{},{"nodeType":1907,"value":17426,"marks":17427,"data":17428},": When an event triggers a DataEngine function, the function executes on the same C-Node where the event is stored—zero data movement",[],{},{"nodeType":12815,"data":17430,"content":17431},{},[17432],{"nodeType":1907,"value":17433,"marks":17434,"data":17435},"3. State Management: Transactional Tables, Vectors, and Consistency",[],{},{"nodeType":1921,"data":17437,"content":17438},{},[17439],{"nodeType":1907,"value":17440,"marks":17441,"data":17442},"Agent state must be persistent, queryable, and consistent. This is where most traditional agent architectures fail—they rely on external databases (e.g., Postgres for state, Pinecone for vectors) which introduces latency, complexity, and data movement.",[],{},{"nodeType":1921,"data":17444,"content":17445},{},[17446],{"nodeType":1907,"value":17447,"marks":17448,"data":17449},"VAST DataBase unifies three capabilities in a single storage layer:",[],{},{"nodeType":13293,"data":17451,"content":17452},{},[17453,17468,17483],{"nodeType":12829,"data":17454,"content":17455},{},[17456],{"nodeType":1921,"data":17457,"content":17458},{},[17459,17464],{"nodeType":1907,"value":17460,"marks":17461,"data":17463},"OLTP: ",[17462],{"type":12906},{},{"nodeType":1907,"value":17465,"marks":17466,"data":17467},"Transactional state (agent metadata, task status, workflow state)",[],{},{"nodeType":12829,"data":17469,"content":17470},{},[17471],{"nodeType":1921,"data":17472,"content":17473},{},[17474,17479],{"nodeType":1907,"value":17475,"marks":17476,"data":17478},"OLAP:",[17477],{"type":12906},{},{"nodeType":1907,"value":17480,"marks":17481,"data":17482}," Analytical queries (aggregate agent performance, system metrics)",[],{},{"nodeType":12829,"data":17484,"content":17485},{},[17486],{"nodeType":1921,"data":17487,"content":17488},{},[17489,17494],{"nodeType":1907,"value":17490,"marks":17491,"data":17493},"Vector storage:",[17492],{"type":12906},{},{"nodeType":1907,"value":17495,"marks":17496,"data":17497}," Embeddings for RAG, semantic search, and memory retrieval",[],{},{"nodeType":1921,"data":17499,"content":17500},{},[17501],{"nodeType":1907,"value":17502,"marks":17503,"data":17504},"The DataBase is built on VAST’s disaggregated storage architecture. Data is stored in columnar format on D-Nodes, with multiple layers of indexing:",[],{},{"nodeType":12952,"data":17506,"content":17507},{},[17508,17523,17538],{"nodeType":12829,"data":17509,"content":17510},{},[17511],{"nodeType":1921,"data":17512,"content":17513},{},[17514,17519],{"nodeType":1907,"value":17515,"marks":17516,"data":17518},"Row-based index: ",[17517],{"type":12906},{},{"nodeType":1907,"value":17520,"marks":17521,"data":17522},"For transactional lookups (e.g., “fetch state for agent_id=123”)",[],{},{"nodeType":12829,"data":17524,"content":17525},{},[17526],{"nodeType":1921,"data":17527,"content":17528},{},[17529,17534],{"nodeType":1907,"value":17530,"marks":17531,"data":17533},"Column-based index:",[17532],{"type":12906},{},{"nodeType":1907,"value":17535,"marks":17536,"data":17537}," For analytical scans (e.g., “count all agents with status=failed”)",[],{},{"nodeType":12829,"data":17539,"content":17540},{},[17541],{"nodeType":1921,"data":17542,"content":17543},{},[17544,17549],{"nodeType":1907,"value":17545,"marks":17546,"data":17548},"Vector index:",[17547],{"type":12906},{},{"nodeType":1907,"value":17550,"marks":17551,"data":17552}," approximate nearest neighbor search for embeddings",[],{},{"nodeType":1921,"data":17554,"content":17555},{},[17556],{"nodeType":1907,"value":17557,"marks":17558,"data":17560},"Vector Operations at Scale",[17559],{"type":12906},{},{"nodeType":1921,"data":17562,"content":17563},{},[17564],{"nodeType":1907,"value":17565,"marks":17566,"data":17567},"The vector storage subsystem is designed for trillion-scale vector search. Key characteristics include:",[],{},{"nodeType":12952,"data":17569,"content":17570},{},[17571,17586,17601],{"nodeType":12829,"data":17572,"content":17573},{},[17574],{"nodeType":1921,"data":17575,"content":17576},{},[17577,17582],{"nodeType":1907,"value":17578,"marks":17579,"data":17581},"Parallel updates",[17580],{"type":12906},{},{"nodeType":1907,"value":17583,"marks":17584,"data":17585},": Unlike traditional vector DBs that require full re-indexing, VAST’s parallel architecture allows concurrent inserts without locking the entire index",[],{},{"nodeType":12829,"data":17587,"content":17588},{},[17589],{"nodeType":1921,"data":17590,"content":17591},{},[17592,17597],{"nodeType":1907,"value":17593,"marks":17594,"data":17596},"Freshness of data: ",[17595],{"type":12906},{},{"nodeType":1907,"value":17598,"marks":17599,"data":17600},"All of the data is searchable, including data that was not already converted to the vectorDB",[],{},{"nodeType":12829,"data":17602,"content":17603},{},[17604],{"nodeType":1921,"data":17605,"content":17606},{},[17607,17612],{"nodeType":1907,"value":17608,"marks":17609,"data":17611},"Trillion-vector scale",[17610],{"type":12906},{},{"nodeType":1907,"value":17613,"marks":17614,"data":17615},": No sharding and no performance degradation at scale",[],{},{"nodeType":12815,"data":17617,"content":17618},{},[17619],{"nodeType":1907,"value":17620,"marks":17621,"data":17622},"4. The Memory Hierarchy: KV Cache Offloading for GPU Efficiency and Endless Context",[],{},{"nodeType":1921,"data":17624,"content":17625},{},[17626],{"nodeType":1907,"value":17627,"marks":17628,"data":17629},"In multi-agent systems, the GPU inference layer is the most expensive and constrained resource. When thousands of agents are simultaneously calling LLM reasoning services, GPU utilization becomes the critical bottleneck that determines system throughput and cost efficiency.",[],{},{"nodeType":1921,"data":17631,"content":17632},{},[17633,17636,17643],{"nodeType":1907,"value":14460,"marks":17634,"data":17635},[],{},{"nodeType":1916,"data":17637,"content":17638},{"uri":12973},[17639],{"nodeType":1907,"value":17640,"marks":17641,"data":17642},"KV cache is the key to unlocking high GPU utilization,",[],{},{"nodeType":1907,"value":17644,"marks":17645,"data":17646}," reducing latency of response, and the ability to support endless context conversations that multi-agent systems require.",[],{},{"nodeType":1921,"data":17648,"content":17649},{},[17650],{"nodeType":1907,"value":17651,"marks":17652,"data":17654},"The GPU Utilization Problem",[17653],{"type":12906},{},{"nodeType":1921,"data":17656,"content":17657},{},[17658],{"nodeType":1907,"value":17659,"marks":17660,"data":17661},"A GPU’s compute capacity is wasted if its memory (HBM) fills up before its processors are fully utilized. ",[],{},{"nodeType":1921,"data":17663,"content":17664},{},[17665],{"nodeType":1907,"value":17666,"marks":17667,"data":17668},"Long-context inference on models like Llama 3.1 70B faces different bottlenecks across its two phases. The prefill phase to compute a 128K-token KV cache (~40 GB in FP16, 50% of an H100 80GB’s memory) saturates tensor cores at 60-90% utilization—expensive computation you’d want to reuse whenever possible. The subsequent autoregressive decode phase becomes memory-bound, dropping to only 20-30% utilization as each step fetches the entire KV cache but performs minimal computation. With model weights at ~140 GB in FP16, a 4×H100 80GB configuration (320 GB total HBM) leaves only ~20-25 GB per GPU for KV cache after accounting for operational overhead. This memory constraint severely limits concurrent long-context requests, forcing deployments to provision 3-5× more GPUs than compute needs would suggest—purely for memory capacity to hold KV caches. Even with optimizations like FP8 quantization, continuous batching, and multi-query attention, memory capacity remains the primary scaling constraint.",[],{},{"nodeType":1921,"data":17670,"content":17671},{},[17672],{"nodeType":1907,"value":17673,"marks":17674,"data":17675},"The result: you’re paying for a $30,000 GPU but only using a fraction of its processing power because memory is the bottleneck, not compute. At scale, this means you need 3-5x more GPUs than you should, purely due to memory constraints.",[],{},{"nodeType":1921,"data":17677,"content":17678},{},[17679],{"nodeType":1907,"value":17680,"marks":17681,"data":17683},"The Solution: KV Cache Offloading",[17682],{"type":12906},{},{"nodeType":1921,"data":17685,"content":17686},{},[17687],{"nodeType":1907,"value":17688,"marks":17689,"data":17690},"By offloading the KV cache from GPU HBM to VAST’s high-speed storage fabric, you break the memory bottleneck:",[],{},{"nodeType":12952,"data":17692,"content":17693},{},[17694,17704,17714],{"nodeType":12829,"data":17695,"content":17696},{},[17697],{"nodeType":1921,"data":17698,"content":17699},{},[17700],{"nodeType":1907,"value":17701,"marks":17702,"data":17703},"Hot cache blocks (actively being used for generation) stay in GPU HBM",[],{},{"nodeType":12829,"data":17705,"content":17706},{},[17707],{"nodeType":1921,"data":17708,"content":17709},{},[17710],{"nodeType":1907,"value":17711,"marks":17712,"data":17713},"Cold cache blocks (common prefixes, older context, inactive requests) are moved to VAST storage",[],{},{"nodeType":12829,"data":17715,"content":17716},{},[17717],{"nodeType":1921,"data":17718,"content":17719},{},[17720],{"nodeType":1907,"value":17721,"marks":17722,"data":17723},"When needed, cold blocks are fetched back to HBM in under 1ms",[],{},{"nodeType":1921,"data":17725,"content":17726},{},[17727],{"nodeType":1907,"value":17728,"marks":17729,"data":17730},"This dramatically increases GPU utilization because memory is no longer the constraint. You can now serve 50-100+ concurrent agent requests per GPU instead of 10-20, fully utilizing the GPU’s compute capacity.",[],{},{"nodeType":1921,"data":17732,"content":17733},{},[17734],{"nodeType":1907,"value":17735,"marks":17736,"data":17738},"Enabling Endless Context for Multi-Agent Systems",[17737],{"type":12906},{},{"nodeType":1921,"data":17740,"content":17741},{},[17742],{"nodeType":1907,"value":17743,"marks":17744,"data":17745},"Multi-agent systems require agents to maintain long-running context across multiple interactions. A ResearchAgent might accumulate more than 100,000 tokens of context over hours of operation. A ConversationAgent might maintain weeks of chat history. Without KV cache offloading, this is impossible—the context simply won’t fit in GPU memory.",[],{},{"nodeType":1921,"data":17747,"content":17748},{},[17749],{"nodeType":1907,"value":17750,"marks":17751,"data":17752},"With KV cache offloading:",[],{},{"nodeType":12952,"data":17754,"content":17755},{},[17756,17766,17776],{"nodeType":12829,"data":17757,"content":17758},{},[17759],{"nodeType":1921,"data":17760,"content":17761},{},[17762],{"nodeType":1907,"value":17763,"marks":17764,"data":17765},"Agents can maintain effectively unlimited context length—bounded only by storage, not GPU memory",[],{},{"nodeType":12829,"data":17767,"content":17768},{},[17769],{"nodeType":1921,"data":17770,"content":17771},{},[17772],{"nodeType":1907,"value":17773,"marks":17774,"data":17775},"Long-running agents don’t need to truncate or summarize their context to fit memory constraints",[],{},{"nodeType":12829,"data":17777,"content":17778},{},[17779],{"nodeType":1921,"data":17780,"content":17781},{},[17782],{"nodeType":1907,"value":17783,"marks":17784,"data":17785},"Multi-turn reasoning tasks can operate over massive context windows without performance degradation",[],{},{"nodeType":1921,"data":17787,"content":17788},{},[17789],{"nodeType":1907,"value":17790,"marks":17791,"data":17793},"The Technical Mechanism",[17792],{"type":12906},{},{"nodeType":1921,"data":17795,"content":17796},{},[17797],{"nodeType":1907,"value":17798,"marks":17799,"data":17800},"VAST achieves KV cache offloading by integrating with inference frameworks (e.g., NVIDIA Dynamo, LMCache) at the KV cache layer:",[],{},{"nodeType":12952,"data":17802,"content":17803},{},[17804,17814,17824,17834,17844],{"nodeType":12829,"data":17805,"content":17806},{},[17807],{"nodeType":1921,"data":17808,"content":17809},{},[17810],{"nodeType":1907,"value":17811,"marks":17812,"data":17813},"The inference engine generates KV cache blocks during forward passes",[],{},{"nodeType":12829,"data":17815,"content":17816},{},[17817],{"nodeType":1921,"data":17818,"content":17819},{},[17820],{"nodeType":1907,"value":17821,"marks":17822,"data":17823},"The cache manager (modified to use VAST as backing storage) keeps frequently-accessed blocks in GPU HBM",[],{},{"nodeType":12829,"data":17825,"content":17826},{},[17827],{"nodeType":1921,"data":17828,"content":17829},{},[17830],{"nodeType":1907,"value":17831,"marks":17832,"data":17833},"When HBM fills up, cold blocks are evicted to VAST storage via high-speed NVMe and RDMA",[],{},{"nodeType":12829,"data":17835,"content":17836},{},[17837],{"nodeType":1921,"data":17838,"content":17839},{},[17840],{"nodeType":1907,"value":17841,"marks":17842,"data":17843},"On a cache miss, the block is fetched from VAST storage with sub-millisecond latency",[],{},{"nodeType":12829,"data":17845,"content":17846},{},[17847],{"nodeType":1921,"data":17848,"content":17849},{},[17850],{"nodeType":1907,"value":17851,"marks":17852,"data":17853},"Prefetch hints and access patterns allow proactive loading of likely-needed blocks",[],{},{"nodeType":1921,"data":17855,"content":17856},{},[17857],{"nodeType":1907,"value":17858,"marks":17859,"data":17860},"Technically, there is a trade-off when offloading KV cache to external storage: generation latency increases by 1-2ms per cache miss. 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As costs add up fast and GPU efficiency becomes a bottleneck, KV cache offloading is a crucial component in multi-agent deployments.",[],{},{"nodeType":12815,"data":17917,"content":17918},{},[17919],{"nodeType":1907,"value":17920,"marks":17921,"data":17922},"5. Security and Isolation: Identity, Permissions, and Audit",[],{},{"nodeType":1921,"data":17924,"content":17925},{},[17926],{"nodeType":1907,"value":17927,"marks":17928,"data":17929},"When thousands of agents are executing autonomously, security cannot be an afterthought. The traditional approach—giving each service a long-lived API key or shared credentials—breaks down catastrophically at scale. A compromised key becomes a skeleton key to your entire system. Credential rotation becomes an operational nightmare. And when something goes wrong, tracing which agent did what becomes nearly impossible.",[],{},{"nodeType":1921,"data":17931,"content":17932},{},[17933],{"nodeType":1907,"value":17934,"marks":17935,"data":17936},"The alternative is to treat agents as first-class principals in your security model, with the same rigor you’d apply to human users or external services. 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This new Series F round was led by Drive Capital and included many of the world’s most respected infrastructure investors, such as Access Industries, NVIDIA, Fidelity Management & Research Company, NEA, and more. Between the primary company investment and secondary sales of VAST stock, this round represents nearly $1B of investment and welcomes VAST into an elite circle of next-generation companies who’ve found product-market fit in the $100T AI market that has emerged seemingly overnight.\n\nGiven this unique position in the market, I thought I would take a moment to provide some perspective on how we’ve gotten here.\n\nTo start, it’s fair to say that VAST is working hard to build a generational company at the center of the data and AI revolution. From our perspective, generational companies share three fundamental qualities:",[],{},{"nodeType":12952,"data":29785,"content":29786},{},[29787,29797,29807],{"nodeType":12829,"data":29788,"content":29789},{},[29790],{"nodeType":1921,"data":29791,"content":29792},{},[29793],{"nodeType":1907,"value":29794,"marks":29795,"data":29796},"They build products that are 10x better than previous approaches",[],{},{"nodeType":12829,"data":29798,"content":29799},{},[29800],{"nodeType":1921,"data":29801,"content":29802},{},[29803],{"nodeType":1907,"value":29804,"marks":29805,"data":29806},"They address a market disruption that causes customers to abandon status quo",[],{},{"nodeType":12829,"data":29808,"content":29809},{},[29810],{"nodeType":1921,"data":29811,"content":29812},{},[29813],{"nodeType":1907,"value":29814,"marks":29815,"data":29816},"They out-execute, turning product-market fit and timing into a sustainable business",[],{},{"nodeType":1921,"data":29818,"content":29819},{},[29820,29824,29829],{"nodeType":1907,"value":29821,"marks":29822,"data":29823},"Unsurprisingly, these qualities combine to create the foundation of the VAST story and the thesis of our long-term potential. It turns out this mix is very hard to perfect, and as we were working through the investment and sharing our story to some of the most respected technology investors, we kept hearing back from them the same phrase … “N=1”.\n\nWhen something is described as “N=1,” it’s a signal that indicates that someone has ",[],{},{"nodeType":1907,"value":29825,"marks":29826,"data":29828},"“never seen anything like this before.”",[29827],{"type":12385},{},{"nodeType":1907,"value":29830,"marks":29831,"data":29832}," A category of one. Today, I’ll attempt to explain N=1 in terms of both VAST’s business posture, as well as our technology advantage.",[],{},{"nodeType":12356,"data":29834,"content":29835},{},[29836],{"nodeType":1907,"value":29837,"marks":29838,"data":29839},"N=1: The VAST Data business story",[],{},{"nodeType":1921,"data":29841,"content":29842},{},[29843],{"nodeType":1907,"value":29844,"marks":29845,"data":29846},"Since the dawn of ChatGPT, VAST Data’s business has grown exponentially. Six years earlier, we started working on a next-generation parallel systems architecture that found itself in the right time and right place when the world discovered the commercial application of deep learning. During periods of exponential growth, nearly every growth story is punctuated by a lack of profitability and significant burn of venture capital. To see examples of this, look no further than today’s AI labs, which are each growing their business like crazy while burning historic levels of venture capital on R&D.",[],{},{"nodeType":1921,"data":29848,"content":29849},{},[29850,29854,29859,29863,29868,29872,29877],{"nodeType":1907,"value":29851,"marks":29852,"data":29853},"The tradeoff between corporate efficiency and growth is not limited to AI labs. 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Not surprisingly, there’s a spectrum of performance as companies get measured by the Rule of 40 — where growth can be traded for efficiency or vice versa, but the sum of these numbers is the measure of how promising a business is.\n\nIf 40% is the benchmark of a “good” company, the best ten $10B+ public software companies (ranked by Rule of X performance) average out to 61%. What excites investors about VAST is our unprecedented mix of growth and profitability, demonstrating to the world that a radically disruptive product and focused team can break fundamental business tradeoffs. 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Just as with the funds that came from our series B, C, D, and E, the money invested into VAST in our Series F will go right into the bank and sit alongside our growing cash reserves while they gain interest. In reality, public financing rounds serve two primary purposes for our company:",[],{},{"nodeType":13293,"data":29936,"content":29937},{},[29938,29960],{"nodeType":12829,"data":29939,"content":29940},{},[29941],{"nodeType":1921,"data":29942,"content":29943},{},[29944,29948,29956],{"nodeType":1907,"value":29945,"marks":29946,"data":29947},"Each investment is a public and independent validation of our progress, and serves as a signal to the world that we’ve raised the bar yet again because of the worldwide adoption of ",[],{},{"nodeType":1916,"data":29949,"content":29951},{"uri":29950},"..\u002F..\u002Fplatform\u002Fai-os",[29952],{"nodeType":1907,"value":29953,"marks":29954,"data":29955},"VAST’s AI Operating System",[],{},{"nodeType":1907,"value":29957,"marks":29958,"data":29959},". 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At the time, OpenAI was just getting off the ground and NVIDIA was just starting to ship the world's first deep learning supercomputer, the DGX. Since then, parallel data processing has been more and more critical to the world’s AI endeavors. To put this into perspective, the AI factories that VAST supports today have over 1 ",[],{},{"nodeType":1907,"value":29994,"marks":29995,"data":29997},"billion ",[29996],{"type":12906},{},{"nodeType":1907,"value":29999,"marks":30000,"data":30001},"CUDA cores or over 1 ",[],{},{"nodeType":1907,"value":30003,"marks":30004,"data":30006},"million ",[30005],{"type":12906},{},{"nodeType":1907,"value":30008,"marks":30009,"data":30010},"tensor cores, all accessing a single VAST data platform.",[],{},{"nodeType":1921,"data":30012,"content":30013},{},[30014],{"nodeType":1907,"value":30015,"marks":30016,"data":30017},"The infrastructure we support does not stop scaling. As large-scale agency and agent swarms define the next generation of computing, all the world’s data infrastructure will need to stand up and answer the call of this level of parallelism. However, the universal need for scale was not so obvious until reasoning models and agents emerged in 2024-2025, and this was the spark that ignited a global re-platforming of the world’s data systems and has led our customers to conclude that VAST’s AI Operating System is critical to achieving future scaling ambitions.",[],{},{"nodeType":1921,"data":30019,"content":30020},{},[30021,30025,30030],{"nodeType":1907,"value":30022,"marks":30023,"data":30024},"We’ve been students of the predictions made by Ark Capital for the last 5 years, a firm that took an early stance on the impact of deep learning that is now undeniable. 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Our ambition and capability leads us to invent across a number of technology categories. The result is one platform designed to do many things very well: file, object and block storage, events, EDW, vectors, runtimes, agents.\n \nVAST is not a category killer. The broad reach of VAST’s AI OS establishes us as a",[],{},{"nodeType":1907,"value":30172,"marks":30173,"data":30175}," killer of categories",[30174],{"type":12385},{},{"nodeType":1907,"value":30177,"marks":30178,"data":30179},". Never in the history of data center infrastructure has a single software stack had this much best-in-class capability. The scale and tradeoff-breaking nature of the DASE architecture makes all of this now practical. 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whisper.\na stop sign.\na prompt.\nan answer.\nan idea.\nan altogether new ",{"data":30526,"marks":30527,"value":30529,"nodeType":1907},{},[30528],{"type":12906},"discovery",{"data":30531,"marks":30532,"value":1920,"nodeType":1907},{},[],{"data":30534,"content":30535,"nodeType":1921},{},[30536],{"data":30537,"marks":30538,"value":30539,"nodeType":1907},{},[],"Every age of technical innovation is defined by an exponential curve of progress. ",{"data":30541,"content":30542,"nodeType":1921},{},[30543],{"data":30544,"marks":30545,"value":30546,"nodeType":1907},{},[],"In 2010, Apple introduced the world to the idea that a computer could listen and respond to natural language. “Hey, Siri.” In 2012, Google found cats. In 2015, DARPA closed the books on their robotics challenge, sufficiently convinced that we could build autonomous systems. In 2020, the first autonomous taxis were approved for driverless ride hailing. Today, GPTs are powering the fastest growing internet applications of all time.",{"data":30548,"content":30549,"nodeType":1921},{},[30550],{"data":30551,"marks":30552,"value":30553,"nodeType":1907},{},[],"And this is only the beginning.",{"data":30555,"content":30556,"nodeType":1921},{},[30557],{"data":30558,"marks":30559,"value":30560,"nodeType":1907},{},[],"The age of deep learning is here. This age will be defined not by digital transformation, where humans make sense of data we’ve stored in machines. Deep learning is an age of analog transformation where machines will make sense of analog inputs that come from the natural world: LiDAR, sound, video, instrument data… even a text. Complex neural networks and powerful GPUs* are now capable of processing data which was once unable to be understood without a human-in-the-loop. AI is combining with unstructured and data-rich streams of data to learn and they are starting to reason about the natural world.",{"data":30562,"content":30563,"nodeType":1921},{},[30564],{"data":30565,"marks":30566,"value":30567,"nodeType":1907},{},[],"As with every age of technology, deep learning is creating new value.",{"data":30569,"content":30570,"nodeType":12952},{},[30571,30581,30591],{"data":30572,"content":30573,"nodeType":12829},{},[30574],{"data":30575,"content":30576,"nodeType":1921},{},[30577],{"data":30578,"marks":30579,"value":30580,"nodeType":1907},{},[],"For society, deep learning will help us further automate mundane tasks and continue to improve quality of life.",{"data":30582,"content":30583,"nodeType":12829},{},[30584],{"data":30585,"content":30586,"nodeType":1921},{},[30587],{"data":30588,"marks":30589,"value":30590,"nodeType":1907},{},[],"As machines become more capable, they will also be able to reason about the natural world and create new discoveries in the areas of math, science and even the arts. AI-discovered advancements for clean energy, food abundance and human health are on the immediate horizon.",{"data":30592,"content":30593,"nodeType":12829},{},[30594],{"data":30595,"content":30596,"nodeType":1921},{},[30597],{"data":30598,"marks":30599,"value":30600,"nodeType":1907},{},[],"From a business perspective, generative AI and deep learning have the potential to raise global GDP by as much as 7% (or $7T) according to Goldman Sachs**",{"data":30602,"content":30603,"nodeType":1921},{},[30604],{"data":30605,"marks":30606,"value":30607,"nodeType":1907},{},[],"Deep learning is also a technological shift that will create new winners and losers across the business landscape. Ford, IBM, Dell, Google, Amazon – these companies all became market leaders on the back of a major technological shift that contributed to the obsolescence of legacy market players. By delivering on the promise of automation and superintelligence, deep learning is a catalyst that can redefine the business landscape arguably more than any preceding technology wave.",{"data":30609,"content":30610,"nodeType":1921},{},[30611],{"data":30612,"marks":30613,"value":30614,"nodeType":1907},{},[],"Today, VAST Data announced that it has nearly tripled its valuation over the last two years and is now worth $9.1 billion. We are proud to add new growth-stage and cross-over investors such as Fidelity Management & Research Company, BOND Capital, NEA and Drive Capital to this amazing story VAST is writing.",{"data":30616,"content":30617,"nodeType":12952},{},[30618,30628],{"data":30619,"content":30620,"nodeType":12829},{},[30621],{"data":30622,"content":30623,"nodeType":1921},{},[30624],{"data":30625,"marks":30626,"value":30627,"nodeType":1907},{},[],"For those of you who are already familiar with our story, know that we have been continuing our track record of operating on a cash-flow positive basis, so this funding is simply being used to raise awareness of VAST and our mission.",{"data":30629,"content":30630,"nodeType":12829},{},[30631],{"data":30632,"content":30633,"nodeType":1921},{},[30634],{"data":30635,"marks":30636,"value":30637,"nodeType":1907},{},[],"For those of you who are new to our story, allow me to break it down for you…",{"data":30639,"content":30640,"nodeType":12356},{},[30641],{"data":30642,"marks":30643,"value":30644,"nodeType":1907},{},[],"Who is VAST Data?",{"data":30646,"content":30647,"nodeType":1921},{},[30648],{"data":30649,"marks":30650,"value":30651,"nodeType":1907},{},[],"VAST is a team of doers. A team determined to challenge the status quo and bring first-principles thinking to problem solving. At over 700 people strong, VAST is a global force that is taking its mission to all reaches of the world.",{"data":30653,"content":30654,"nodeType":1921},{},[30655],{"data":30656,"marks":30657,"value":30658,"nodeType":1907},{},[],"Our product mission is to build a data platform for the age of deep learning. The VAST Data Platform integrates unstructured data management services with a structured data environment and a compute runtime that is intended on refining unstructured data into query-able, actionable information. If the superpower of Deep Learning is the transformation of unstructured data into structured data, the VAST Data Platform is the software-based real-time engine that sits between AI applications and the HW layer that drives modern computing and storage.",{"data":30660,"content":30678,"nodeType":13214},{"target":30661},{"metadata":30662,"sys":30665,"fields":30672},{"tags":30663,"concepts":30664},[],[],{"space":30666,"id":30668,"type":934,"createdAt":30669,"updatedAt":30669,"environment":30670,"publishedVersion":847,"revision":1136,"locale":673},{"sys":30667},{"type":657,"linkType":658,"id":659},"7Idbtq6awWg6Kanavwv663","2023-12-05T23:43:28.597Z",{"sys":30671},{"id":665,"type":657,"linkType":666},{"title":30477,"description":30477,"file":30673},{"url":30674,"details":30675,"fileName":30513,"contentType":30514},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F7Idbtq6awWg6Kanavwv663\u002F8dfb98217bdb8380a1190aa8185c787b\u002Fa-new-value.gif",{"size":30676,"image":30677},825835,{"width":8628,"height":8629},[],{"data":30680,"content":30681,"nodeType":1921},{},[30682,30686,30692],{"data":30683,"marks":30684,"value":30685,"nodeType":1907},{},[],"In 2019, we announced the first distributed systems architecture concept in the market since the advent of the Google File System (which Google introduced to the world in 2003). This new concept, which we call ",{"data":30687,"content":30688,"nodeType":1916},{"uri":7401},[30689],{"data":30690,"marks":30691,"value":30085,"nodeType":1907},{},[],{"data":30693,"marks":30694,"value":30695,"nodeType":1907},{},[],", introduces radical levels of scale, simplicity, affordability and enterprise data management for distributed computing. While the first application of this system was to serve files and objects to unstructured data applications, we’re on a journey to build a system that continuously computes on data at hyperscale across a collection of customer compute environments that extends from edge to cloud.",{"data":30697,"content":30698,"nodeType":12356},{},[30699],{"data":30700,"marks":30701,"value":30702,"nodeType":1907},{},[],"Why are we at the center of today’s AI action?",{"data":30704,"content":30705,"nodeType":1921},{},[30706],{"data":30707,"marks":30708,"value":30709,"nodeType":1907},{},[],"Data is the fuel of AI.\nHigh quality data is the foundation for the most successful AI models.",{"data":30711,"content":30712,"nodeType":1921},{},[30713],{"data":30714,"marks":30715,"value":30716,"nodeType":1907},{},[],"Our strategy has been to build enterprise data infrastructure that is not only used in the context of model training and inference, but we’re also building data infrastructure which can simplify and scale data access for every application. Customers today use us as the basis of their enterprise application containerization efforts, for enterprise file management, for archives, for high performance computing and more. To date, VAST has been entrusted by customers all over the world with over 10 exabytes of their critical enterprise data. As an enterprise systems’ provider, the VAST thesis has been to modernize every aspect of data and database storage so customers can easily “bring AI to their data” vs “bring their data to some complicated AI data platform”. We’ve now seen this story play out time and time again – with customers such as Disney, Zoom, NASA, the US Department of Energy and more. Our valuation announcement today also serves as testament to the fact that this strategy is working.",{"data":30718,"content":30719,"nodeType":1921},{},[30720],{"data":30721,"marks":30722,"value":30723,"nodeType":1907},{},[],"Put into context, we see a massive opportunity to unlock access to the world’s vast reserves of unstructured data, which (until now) has been static and trapped dormant in old school archives. If structured data represents roughly 5% of the world’s stored data, our belief is that the emergence of deep learning into the enterprise represents an opportunity to grow the enterprise big data opportunity by 20x.",{"data":30725,"content":30744,"nodeType":13214},{"target":30726},{"metadata":30727,"sys":30730,"fields":30737},{"tags":30728,"concepts":30729},[],[],{"space":30731,"id":30733,"type":934,"createdAt":30734,"updatedAt":30734,"environment":30735,"publishedVersion":847,"revision":1136,"locale":673},{"sys":30732},{"type":657,"linkType":658,"id":659},"6lhM3vNzS2fbvQGeQoFdKV","2023-12-05T21:08:31.875Z",{"sys":30736},{"id":665,"type":657,"linkType":666},{"title":30477,"description":30477,"file":30738},{"url":30739,"details":30740,"fileName":30743,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6lhM3vNzS2fbvQGeQoFdKV\u002F77c4e663e660f71a5c6f40ad2c76393c\u002Fa-new-value-1.webp",{"size":30741,"image":30742},57032,{"width":1144,"height":1145},"a-new-value-1.webp",[],{"data":30746,"content":30747,"nodeType":1921},{},[30748],{"data":30749,"marks":30750,"value":30751,"nodeType":1907},{},[],"In business terms, we’ve built one of the fastest-growing technology companies in history. Our focus on storing and protecting the world’s data does not just extend to conventional enterprise customers, but we also build service provider readiness directly into the VAST Data Platform. The combination of hyperscale performance and zero-trust multi-tenancy is why VAST is also being trusted to power today’s new emerging AI clouds.",{"data":30753,"content":30754,"nodeType":1921},{},[30755],{"data":30756,"marks":30757,"value":30758,"nodeType":1907},{},[],"In context, we see a few select companies emerging that are redefining hyperscale in real-time. When surveying the list of funding announcements this year, only 5 companies (of those valued over $5B) have emerged from 2023 with greater than a 100% valuation increase. Interestingly, these are all aligned to the new era of deep learning and represent a new stack we see emerging as a representation of the new value AI is creating in the market.",{"data":30760,"content":30781,"nodeType":13214},{"target":30761},{"metadata":30762,"sys":30765,"fields":30773},{"tags":30763,"concepts":30764},[],[],{"space":30766,"id":30768,"type":934,"createdAt":30769,"updatedAt":30770,"environment":30771,"publishedVersion":811,"revision":901,"locale":673},{"sys":30767},{"type":657,"linkType":658,"id":659},"4jX9zQdoFFPpgiB0t8bMG1","2023-12-05T21:11:34.653Z","2023-12-06T00:47:55.315Z",{"sys":30772},{"id":665,"type":657,"linkType":666},{"title":30477,"description":30477,"file":30774},{"url":30775,"details":30776,"fileName":30780,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4jX9zQdoFFPpgiB0t8bMG1\u002F581be6caa451635478c3764ffa5c3964\u002FAI_Stack_Valuation_-_BLOG.webp",{"size":30777,"image":30778},196136,{"width":30779,"height":30779},1338,"AI Stack Valuation - BLOG.webp",[],{"data":30783,"content":30784,"nodeType":12356},{},[30785],{"data":30786,"marks":30787,"value":30788,"nodeType":1907},{},[],"How is this unique?",{"data":30790,"content":30791,"nodeType":1921},{},[30792],{"data":30793,"marks":30794,"value":30795,"nodeType":1907},{},[],"As mentioned in the title of this blog, we see a new era of value creation that is unfolding in real-time in the market. Yesterday’s big data and machine learning companies are not capable of capturing the new unstructured, big data opportunity. Moreover, as VAST is working with data that does not naturally conform to tables or BI systems, we work in the world’s largest GPU environments.",{"data":30797,"content":30817,"nodeType":13214},{"target":30798},{"metadata":30799,"sys":30802,"fields":30809},{"tags":30800,"concepts":30801},[],[],{"space":30803,"id":30805,"type":934,"createdAt":30806,"updatedAt":30806,"environment":30807,"publishedVersion":847,"revision":1136,"locale":673},{"sys":30804},{"type":657,"linkType":658,"id":659},"cw5Lx7nZcLCQejfBYgVQl","2023-12-05T21:17:10.523Z",{"sys":30808},{"id":665,"type":657,"linkType":666},{"title":30477,"description":30477,"file":30810},{"url":30811,"details":30812,"fileName":30816,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002Fcw5Lx7nZcLCQejfBYgVQl\u002F969db3a08639d679efa91a07eae7d663\u002Fa-new-value-3.webp",{"size":30813,"image":30814},56154,{"width":1144,"height":30815},997,"a-new-value-3.webp",[],{"data":30819,"content":30820,"nodeType":1921},{},[30821],{"data":30822,"marks":30823,"value":30824,"nodeType":1907},{},[],"Interest rates are still very high, and investors are making bets extremely selectively. At a time when some of the world’s most successful data companies trade at a discount vs their previous highs, the market is recognizing that deep learning, GPUs and the data platforms that feed these GPUs are fundamentally changing the relationship between customers, clouds and their data. There’s no surprise that our valuation increase is highly correlated to the world’s leading AI companies, as we’re both seeing massive adoption for data-intensive AI applications across many common customers and AI clouds. On the application side, we also see private company LLM and GenAI application builders also enjoy the same valuation increase.",{"data":30826,"content":30827,"nodeType":12356},{},[30828],{"data":30829,"marks":30830,"value":30831,"nodeType":1907},{},[],"What’s next?",{"data":30833,"content":30834,"nodeType":1921},{},[30835],{"data":30836,"marks":30837,"value":30838,"nodeType":1907},{},[],"As mentioned above – VAST operates its business on a cash-flow positive basis. We’ve managed to build a company which can triple its business annually while not burning through mountains of venture capital. This, in and of itself, is extremely rare. VAST is an independent organization that has built a self-sustaining business powered by innovation, fanatical customer support and an unprecedented AI-driven market opportunity. Without any need for the new capital, this new Series E funding will sit in the bank and collect interest alongside the funding we received from Series B, Series C and Series D.",{"data":30840,"content":30841,"nodeType":1921},{},[30842],{"data":30843,"marks":30844,"value":30845,"nodeType":1907},{},[],"Having said that, there’s still so much to do and we continue to invest as we grow. Zettabytes of data are ripe for refinement, and our unyielding focus is to build out the features and the team behind the VAST Data Platform so that every customer around the world can enjoy its benefits. Over the next twelve months you’ll see a lot more focus on cloud, zero-trust, the AI computational layer and you will see us build out a community of customers and developers who organize around the idea that a new platform concept can breathe life into (new and) old data.",{"data":30847,"content":30848,"nodeType":1921},{},[30849],{"data":30850,"marks":30851,"value":30852,"nodeType":1907},{},[],"As I think about where we are today, I must recognize the effort of the global VAST team and their tireless pursuit of our shared mission. Customer value can’t be created only by the formation of an idea. Successful ideas become successful products in the market when smart and determined teams work hard to transform these dreams into reality. The VAST team is both inspiring to observe and humbling to work with. The future of AI-powered discovery is what drives us, and I’m so looking forward to reading the next chapter of this great story we’re writing.",{"data":30854,"content":30855,"nodeType":1922},{},[30856],{"data":30857,"content":30858,"nodeType":1921},{},[30859,30863,30872],{"data":30860,"marks":30861,"value":30862,"nodeType":1907},{},[],"* also growing on an exponential curve!\n** Goldman Sachs ",{"data":30864,"content":30866,"nodeType":1916},{"uri":30865},"https:\u002F\u002Fwww.goldmansachs.com\u002Fintelligence\u002Fpages\u002Fgenerative-ai-could-raise-global-gdp-by-7-percent.html",[30867],{"data":30868,"marks":30869,"value":30871,"nodeType":1907},{},[30870],{"type":15085},"GenAI 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New Value: Exploring VAST Data's Innovative Approach","VAST, a self-sustaining, cash-flow positive company, triples business annually. Series E funding adds to reserves, enabling continued innovation and global expansion.",{"metadata":31019,"sys":31022,"fields":31029},{"tags":31020,"concepts":31021},[],[],{"space":31023,"id":31025,"type":934,"createdAt":31026,"updatedAt":31026,"environment":31027,"publishedVersion":847,"revision":1136,"locale":673},{"sys":31024},{"type":657,"linkType":658,"id":659},"6sQB9jqqDbI4Dc6RkUStTi","2023-12-05T20:32:12.906Z",{"sys":31028},{"id":665,"type":657,"linkType":666},{"title":30477,"description":30477,"file":31030},{"url":31031,"details":31032,"fileName":30513,"contentType":30514},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6sQB9jqqDbI4Dc6RkUStTi\u002Fbab28af03939bd0ff0873a0bf2914ecf\u002Fa-new-value.gif",{"size":31033,"image":31034},3473681,{"width":1457,"height":1458},{"metadata":31036,"sys":31054,"fields":31065},{"tags":31037,"concepts":31053},[31038,31041,31044,31046,31048,31051],{"sys":31039},{"type":657,"linkType":1502,"id":31040},"ai",{"sys":31042},{"type":657,"linkType":1502,"id":31043},"funnelConsideration",{"sys":31045},{"type":657,"linkType":1502,"id":30457},{"sys":31047},{"type":657,"linkType":1502,"id":30460},{"sys":31049},{"type":657,"linkType":1502,"id":31050},"perspectives",{"sys":31052},{"type":657,"linkType":1502,"id":30463},[],{"space":31055,"id":31057,"type":14,"createdAt":31058,"updatedAt":31059,"environment":31060,"publishedVersion":31062,"revision":1096,"contentType":31063,"locale":673},{"sys":31056},{"type":657,"linkType":658,"id":659},"1PIglBqBRraGe2LD8VeFkr","2023-08-01T16:06:02.432Z","2025-09-19T23:20:40.942Z",{"sys":31061},{"id":665,"type":657,"linkType":666},250,{"sys":31064},{"type":657,"linkType":671,"id":12767},{"title":31066,"slug":31067,"publishedDate":31068,"author":31069,"thumbnailImage":31070,"middleContent":31103,"socialIcons":31840,"hidden":19,"seo":31966},"The Grand Unification Theory of AI Infrastructure","the-grand-unification-theory-of-ai-infrastructure","2023-08-01","Jeff Denworth, Co-Founder",{"metadata":31071,"sys":31074,"fields":31084},{"tags":31072,"concepts":31073},[],[],{"space":31075,"id":31077,"type":14,"createdAt":31078,"updatedAt":31079,"environment":31080,"publishedVersion":1302,"revision":752,"contentType":31082,"locale":673},{"sys":31076},{"type":657,"linkType":658,"id":659},"4fz6KTmJ8GCARjfylrpknr","2023-08-01T16:06:49.775Z","2023-08-31T00:51:06.876Z",{"sys":31081},{"id":665,"type":657,"linkType":666},{"sys":31083},{"type":657,"linkType":671,"id":12173},{"entryTitle":9753,"image":31085},{"metadata":31086,"sys":31089,"fields":31097},{"tags":31087,"concepts":31088},[],[],{"space":31090,"id":31092,"type":934,"createdAt":31093,"updatedAt":31094,"environment":31095,"publishedVersion":865,"revision":901,"locale":673},{"sys":31091},{"type":657,"linkType":658,"id":659},"6L5HoA0onJ6UFp9smkGFlS","2023-08-02T01:21:16.604Z","2023-08-31T00:51:00.248Z",{"sys":31096},{"id":665,"type":657,"linkType":666},{"title":9753,"description":9753,"file":31098},{"url":31099,"details":31100,"fileName":3271,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6L5HoA0onJ6UFp9smkGFlS\u002F0d160e2384b975bb974ecf693ccb93b6\u002Fvast-data-platform.webp",{"size":31101,"image":31102},68570,{"width":1144,"height":1145},{"data":31104,"content":31105,"nodeType":1922},{},[31106,31126,31133,31140,31156,31163,31170,31178,31185,31192,31200,31207,31214,31229,31237,31261,31269,31276,31283,31290,31297,31330,31338,31360,31368,31375,31382,31405,31412,31420,31442,31450,31457,31464,31471,31478,31494,31527,31550,31557,31565,31573,31580,31603,31619,31626,31633,31640,31648,31671,31693,31701,31708,31715,31738,31745,31774,31781,31788,31795,31803,31826,31833],{"data":31107,"content":31108,"nodeType":1921},{},[31109,31113,31122],{"data":31110,"marks":31111,"value":31112,"nodeType":1907},{},[],"Blog titles are tough to get right. Some other options I considered for today’s ",{"data":31114,"content":31116,"nodeType":1916},{"uri":31115},"https:\u002F\u002Fvastdata.com\u002Fpress-releases\u002Fvast-unveils-vast-data-platform",[31117],{"data":31118,"marks":31119,"value":31121,"nodeType":1907},{},[31120],{"type":15085},"momentous announcement",{"data":31123,"marks":31124,"value":31125,"nodeType":1907},{},[],"... “The Master Plan, Part Un & Deux”, “Breaking Tradeoffs: Part II”. Having said that, given how many conversations I’ve had over the past few days about collapsing the IT stack for AI, my mind keeps wandering back to universal constructs and the power of stack unification. Get ready for words, this blog is a doozy.",{"data":31127,"content":31128,"nodeType":1921},{},[31129],{"data":31130,"marks":31131,"value":31132,"nodeType":1907},{},[],"\u003Cclears throat, adopts Ted-talk speaking style>",{"data":31134,"content":31135,"nodeType":1921},{},[31136],{"data":31137,"marks":31138,"value":31139,"nodeType":1907},{},[],"All the things.",{"data":31141,"content":31142,"nodeType":1921},{},[31143,31147,31152],{"data":31144,"marks":31145,"value":31146,"nodeType":1907},{},[],"This is the aim of a data platform... to be all the things for data-driven applications (compute services, data services and storage services). Today, we introduced the world to a revolutionary system concept that makes AI computing radically easier, more scalable and more efficient than ever before: ",{"data":31148,"marks":31149,"value":31151,"nodeType":1907},{},[31150],{"type":12906},"The VAST Data Platform",{"data":31153,"marks":31154,"value":31155,"nodeType":1907},{},[],". This is an idea conceived in late 2015 and purpose-built for the age of AI; it’s a concept we then built into a company that was founded the same month and year that OpenAI was founded. ",{"data":31157,"content":31158,"nodeType":1921},{},[31159],{"data":31160,"marks":31161,"value":31162,"nodeType":1907},{},[],"We are VAST Data, and this is the story we’ve wanted to tell you since December of 2015.",{"data":31164,"content":31165,"nodeType":1921},{},[31166],{"data":31167,"marks":31168,"value":31169,"nodeType":1907},{},[],"The idea is relatively simple.",{"data":31171,"content":31172,"nodeType":1921},{},[31173],{"data":31174,"marks":31175,"value":31177,"nodeType":1907},{},[31176],{"type":12385},"Let’s build a global computing and storage platform that organizes data from the natural world, a machine (in software) that enriches and applies structure to unstructured data to give AI applications the tools needed to derive greater insights from data.",{"data":31179,"content":31180,"nodeType":1921},{},[31181],{"data":31182,"marks":31183,"value":31184,"nodeType":1907},{},[],"To truly build such a system, a system engineered for continuous global inference and learning, we needed to throw convention out the window and start from a blank sheet of paper. In doing so, the VAST Data Platform has been conceived as a single monolithic data-driven system that seeks to break so many of the tradeoffs that have led to decades of compromise and point solutions.",{"data":31186,"content":31187,"nodeType":1921},{},[31188],{"data":31189,"marks":31190,"value":31191,"nodeType":1907},{},[],"In this blog, I’ll attempt to articulate the infrastructure tradeoffs that we’ve been working to eliminate now that we are able to unveil our entire Data Platform concept. Keeping with the theme of universal constructs, let’s talk about gravitational collapses.",{"data":31193,"content":31194,"nodeType":12356},{},[31195],{"data":31196,"marks":31197,"value":31199,"nodeType":1907},{},[31198],{"type":12906},"Collapsing Storage Tiers",{"data":31201,"content":31202,"nodeType":1921},{},[31203],{"data":31204,"marks":31205,"value":31206,"nodeType":1907},{},[],"When we started, we realized that deep learning is essentially applied statistics… an approach to computing where vast datasets can fuel learning engines by giving the best representation of the natural world. Flash is an essential component for deep learning, for this reason there is no HDD-based solution that Nvidia allows, for example, for their SuperPOD environments. ",{"data":31208,"content":31209,"nodeType":1921},{},[31210],{"data":31211,"marks":31212,"value":31213,"nodeType":1907},{},[],"When we started VAST, the cost of legacy all-flash infrastructure was 10x the cost of HDD-based alternatives. No one was thinking of flash for the capacity tier. On the other hand, if it would be possible to build archives from flash, there would be no need for any faster storage tiers and it would be possible to just compute right from the archive. At the time, there were many new storage startups working on making all-flash even faster, VAST was the only company making all-flash practically affordable.",{"data":31215,"content":31216,"nodeType":1921},{},[31217,31221,31225],{"data":31218,"marks":31219,"value":31220,"nodeType":1907},{},[],"How do we do it? By combining a new approach to data reduction with an architecture that embraces the idea of using low-cost, laptop-grade flash we’ve built a new approach to data storage infrastructure that is now becoming the file and object foundation of the modern data center… the ",{"data":31222,"marks":31223,"value":3542,"nodeType":1907},{},[31224],{"type":12906},{"data":31226,"marks":31227,"value":31228,"nodeType":1907},{},[],". Formerly referred to as Universal Storage, the efficiency characteristics of this distributed file and object storage system are married with the scale and resilience of the VAST DASE architecture to make it possible to build a single tier of affordable exabyte-scale flash within the data center. When your archive is built from flash, then infrastructure no longer fights against the random read challenges introduced by deep learning and deep data analysis workloads.",{"data":31230,"content":31231,"nodeType":1921},{},[31232],{"data":31233,"marks":31234,"value":31236,"nodeType":1907},{},[31235],{"type":12906},"One tier.",{"data":31238,"content":31260,"nodeType":13214},{"target":31239},{"metadata":31240,"sys":31243,"fields":31250},{"tags":31241,"concepts":31242},[],[],{"space":31244,"id":31246,"type":934,"createdAt":31247,"updatedAt":31247,"environment":31248,"publishedVersion":692,"revision":1136,"locale":673},{"sys":31245},{"type":657,"linkType":658,"id":659},"31iCcf47yjbA7G5E9NuSP2","2023-08-01T16:40:56.157Z",{"sys":31249},{"id":665,"type":657,"linkType":666},{"title":31251,"description":2031,"file":31252},"Diagram 1",{"url":31253,"details":31254,"fileName":31259,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F31iCcf47yjbA7G5E9NuSP2\u002Fd951eefe76cccfb0438a19e4daf2777d\u002Fdiagram_1.webp",{"size":31255,"image":31256},31730,{"width":31257,"height":31258},2005,1030,"diagram 1.webp",[],{"data":31262,"content":31263,"nodeType":12356},{},[31264],{"data":31265,"marks":31266,"value":31268,"nodeType":1907},{},[31267],{"type":12906},"Collapsing Database Tiers",{"data":31270,"content":31271,"nodeType":1921},{},[31272],{"data":31273,"marks":31274,"value":31275,"nodeType":1907},{},[],"The VAST Data Platform offering has taken the market by storm. After selling 10 exabytes of software to power a wide variety of enterprise and research applications, it’s become one of the fastest growing products in IT infrastructure history. In 2019, we were both proud and disappointed that IDC declared that the product was “the storage architecture of the future”... proud because of the prestigious distinction, but disappointed because we knew it could be so much more and we didn’t want the market to make too many preconceptions about who were were to become. ",{"data":31277,"content":31278,"nodeType":1921},{},[31279],{"data":31280,"marks":31281,"value":31282,"nodeType":1907},{},[],"Deep Learning pipelines require a variety of data management tools. Unstructured data is the foundation for storing data that comes from the natural world, and the power of AI is to understand the context of files that have no schema in order to create structure from unstructured data. While file systems are critically important (it’s the only required data system in a SuperPOD, for example), databases are equally important. Unstructured is the raw content, and structure becomes the corresponding that comes from data labeling, data prep and AI inference. ",{"data":31284,"content":31285,"nodeType":1921},{},[31286],{"data":31287,"marks":31288,"value":31289,"nodeType":1907},{},[],"Now - to build a system that can constantly learn, we realized that database services were as critical as unstructured data services and that we needed to challenge fundamental design assumptions of database management systems (DBMSs) in order to create a system that could ingest multi-variant data streams in real-time while also providing the foundation for instantaneous structured queries that could correlate data all the way to the archive. Given the scale we operate at, the concept of a transactional and analytical system needed to be reimagined from the ground up.",{"data":31291,"content":31292,"nodeType":1921},{},[31293],{"data":31294,"marks":31295,"value":31296,"nodeType":1907},{},[],"How do we do it? First, we now offer a tabular format within the system. Tables are simply a new data format presented by the VAST Data Element Store (our storage namespace). This table store can be accessed by a version of SQL we’ve developed (VAST SQL) and also works with other modern query engines such as Apache Spark and Trino. Second, we’ve built a new database architecture that writes and updates small records into a large persistent memory buffer (storage class memory) in record form, and then migrates records into fine-grained columnar database objects that are radically smaller than a classic Parquet or Orc row group. Granularity equals query precision and speed. Since we’ve built a 32KB columnar object that is 4,000x smaller than a conventional table partition, our approach results in the ability to service filtered queries up to 100x faster than legacy data lake approaches. Oh, ACID updates are also easy since our architecture is log-structured (writes in free space) and since it’s easy to update 32KB columnar objects vs re-writing large and versioned Parquet objects. No more database vacuuming, no more worries about updates. ",{"data":31298,"content":31299,"nodeType":1921},{},[31300,31304,31309,31313,31317,31321,31326],{"data":31301,"marks":31302,"value":31303,"nodeType":1907},{},[],"Let’s jump into a time machine and go back to the early ‘70s, the time before data warehouses. If the database systems of the 1960’s and 1970’s were able to transact ",{"data":31305,"marks":31306,"value":31308,"nodeType":1907},{},[31307],{"type":12906},"and ",{"data":31310,"marks":31311,"value":31312,"nodeType":1907},{},[],"scale their data ",{"data":31314,"marks":31315,"value":29873,"nodeType":1907},{},[31316],{"type":12906},{"data":31318,"marks":31319,"value":31320,"nodeType":1907},{},[]," could efficiently handle queries, would technologies such as data warehouses and data lakes ever be needed? Likely not. This is the place we are aiming to get back to by calling our approach to storing and analyzing tabular data ",{"data":31322,"marks":31323,"value":31325,"nodeType":1907},{},[31324],{"type":12906},"the VAST DataBase",{"data":31327,"marks":31328,"value":31329,"nodeType":1907},{},[],". The VAST DataBase is the world’s first transactional system that marries ACID transactional semantics that scale across an nearly-infinite number of tables while enabling the system to query datasets that extend from real-time streams all the way to exabyte scale archives. ",{"data":31331,"content":31332,"nodeType":1921},{},[31333],{"data":31334,"marks":31335,"value":31337,"nodeType":1907},{},[31336],{"type":12906},"One tier.\nOne database.",{"data":31339,"content":31359,"nodeType":13214},{"target":31340},{"metadata":31341,"sys":31344,"fields":31351},{"tags":31342,"concepts":31343},[],[],{"space":31345,"id":31347,"type":934,"createdAt":31348,"updatedAt":31348,"environment":31349,"publishedVersion":847,"revision":1136,"locale":673},{"sys":31346},{"type":657,"linkType":658,"id":659},"40w5Cr5UjfS0nfK1N6fuSH","2023-08-01T16:41:30.643Z",{"sys":31350},{"id":665,"type":657,"linkType":666},{"title":31352,"description":2031,"file":31353},"Diagram 2",{"url":31354,"details":31355,"fileName":31358,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F40w5Cr5UjfS0nfK1N6fuSH\u002Fd7691e7b32c545c77905e952c23976fd\u002Fdiagram_2.webp",{"size":31356,"image":31357},37570,{"width":31257,"height":31258},"diagram 2.webp",[],{"data":31361,"content":31362,"nodeType":12356},{},[31363],{"data":31364,"marks":31365,"value":31367,"nodeType":1907},{},[31366],{"type":12906},"Collapsing Files & Tables",{"data":31369,"content":31370,"nodeType":1921},{},[31371],{"data":31372,"marks":31373,"value":31374,"nodeType":1907},{},[],"For decades, databases have always been independent from the file and object storage systems they’ve been deployed to catalog. Yet, content does not exist in a vacuum, and context has always been managed from some other system of record because no product was ever built to bring together structured and unstructured data into one unified system that could transact, read, analyze and store data all the way to exabyte proportions. Until now.",{"data":31376,"content":31377,"nodeType":1921},{},[31378],{"data":31379,"marks":31380,"value":31381,"nodeType":1907},{},[],"One of the major unifications that happens with the VAST Data Platform is the synthesis of content and context. At a high level, we think of most database services to be extensions of our data catalog concept.",{"data":31383,"content":31384,"nodeType":12952},{},[31385,31395],{"data":31386,"content":31387,"nodeType":12829},{},[31388],{"data":31389,"content":31390,"nodeType":1921},{},[31391],{"data":31392,"marks":31393,"value":31394,"nodeType":1907},{},[],"A social networking site, for example, can store their photos as objects in the VAST DataStore, serve data globally with the VAST DataSpace and keep the enriched metadata in VAST DataBase. In the past, they’d need to build an object storage system, combine this with a CDN and then deploy a series of NoSQL databases to keep track of objects. Now: simple.",{"data":31396,"content":31397,"nodeType":12829},{},[31398],{"data":31399,"content":31400,"nodeType":1921},{},[31401],{"data":31402,"marks":31403,"value":31404,"nodeType":1907},{},[],"A deep learning pipeline requires a series of databases to capture data labels and to build training example indices that are used for managing training pipelines. These systems sit alongside object-based storage archives and parallel scratch file systems for AI scratchpads. Now the DataStore delivers both high-capacity and high performance file access (collapsing tiers) and the DataBase provides the semantic layer for training data that eliminates the need for SQL engines and HDD-based data lakes. The Platform’s Cataloging services ensure the database is never out of sync with the content store. Now: simple.",{"data":31406,"content":31407,"nodeType":1921},{},[31408],{"data":31409,"marks":31410,"value":31411,"nodeType":1907},{},[],"There have been several famous attempts to evolve databases into unstructured databases (my term), and none have succeeded. However, by planning for a unified data management architecture that scales and blurs the lines between transactional analytics, we believe that smart architecture can make the impossible possible.",{"data":31413,"content":31414,"nodeType":1921},{},[31415],{"data":31416,"marks":31417,"value":31419,"nodeType":1907},{},[31418],{"type":12906},"One tier.\nOne database.\nOne dataset.",{"data":31421,"content":31441,"nodeType":13214},{"target":31422},{"metadata":31423,"sys":31426,"fields":31433},{"tags":31424,"concepts":31425},[],[],{"space":31427,"id":31429,"type":934,"createdAt":31430,"updatedAt":31430,"environment":31431,"publishedVersion":847,"revision":1136,"locale":673},{"sys":31428},{"type":657,"linkType":658,"id":659},"4qSJKjwTTqK8EEBRAwxq6","2023-08-01T16:42:01.279Z",{"sys":31432},{"id":665,"type":657,"linkType":666},{"title":31434,"description":2031,"file":31435},"Diagram 3",{"url":31436,"details":31437,"fileName":31440,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4qSJKjwTTqK8EEBRAwxq6\u002F68b2a23b7b3a92e13ab85e1a1eea4c76\u002Fdiagram_3.webp",{"size":31438,"image":31439},41398,{"width":31257,"height":31258},"diagram 3.webp",[],{"data":31443,"content":31444,"nodeType":12356},{},[31445],{"data":31446,"marks":31447,"value":31449,"nodeType":1907},{},[31448],{"type":12906},"Collapsing Namespaces",{"data":31451,"content":31452,"nodeType":1921},{},[31453],{"data":31454,"marks":31455,"value":31456,"nodeType":1907},{},[],"Next, as we thought about deep learning we thought about dataset size and gravity. ",{"data":31458,"content":31459,"nodeType":1921},{},[31460],{"data":31461,"marks":31462,"value":31463,"nodeType":1907},{},[],"Modern deep learning applications learn from data that comes from the natural world. Computer vision (sight), natural language processing (sound), large language models (free text) all combine to create datasets that are so large that customers cannot easily move them across different data centers. How large? VAST works with organizations who have single clusters that reach into the 100PBs scale, and they are now working with us to build toward exabyte-scale. These massive payloads hold the keys to the future by providing the insights that systems can access to learn and infer from data.",{"data":31465,"content":31466,"nodeType":1921},{},[31467],{"data":31468,"marks":31469,"value":31470,"nodeType":1907},{},[],"It’s impossible to easily move petabytes or exabytes of data when you want to ensure global access across a multitude of data centers. Global data replication systems create copies in every data center, which is not necessarily practical when dealing with this level of scale. Generally a single replica copy is sufficient to ensure application availability, and any additional copies are superfluous. On the other hand, cloud-based file and database management tools are more suited to providing global data access by applying caching techniques to make read operations fast at the edge of the network. Reads are easy to handle since the different edges of the network don’t need to worry about consistency or transaction management. Today’s CDN services can have 1,000s of data centers that accelerate reads at the edge - that problem is well solved.",{"data":31472,"content":31473,"nodeType":1921},{},[31474],{"data":31475,"marks":31476,"value":31477,"nodeType":1907},{},[],"The challenge we thought about was the challenge of global transactional consistency enforcement. If you build a federation of systems that all provide the ability to access a global corpus of data, any site that transacts into this data needs to be coordinated across other sites that also want a coherent view of that data. Historically, transactions that happen across a global data namespace need to be synchronized across any number of locations and need to be managed and enforced by some central data owner that is responsible for arbitrating transactions across a global network. While this approach of central transaction management ensures strict consistency, this comes at the cost of transactional performance - since every site then needs to write at the speed of the wide area network as they check their data across the WAN into a remote data owner. While this might work for document management, our conclusion was that a global collection of AI computers could not afford to write at WAN speeds.",{"data":31479,"content":31480,"nodeType":1921},{},[31481,31485,31490],{"data":31482,"marks":31483,"value":31484,"nodeType":1907},{},[],"In an effort to break the tradeoff between strict consistency and high-performance read-write access at the edge of the network, we’ve pioneered a new approach to global data access that we call the ",{"data":31486,"marks":31487,"value":31489,"nodeType":1907},{},[31488],{"type":12906},"VAST DataSpace.",{"data":31491,"marks":31492,"value":31493,"nodeType":1907},{},[]," How do we do it? ",{"data":31495,"content":31496,"nodeType":12952},{},[31497,31507,31517],{"data":31498,"content":31499,"nodeType":12829},{},[31500],{"data":31501,"content":31502,"nodeType":1921},{},[31503],{"data":31504,"marks":31505,"value":31506,"nodeType":1907},{},[],"First, VAST clusters can peer to any other VAST cluster to create a data sharing relationship across global networks. These systems will subscribe to each other’s data with a selective level of granularity, taking the peering down to the path-level (directory-level, bucket-level, table-level). The system provides the ability to create either a syncing relationship between sites or to let any site take a global, writeable snapshot clone of a path in case ",{"data":31508,"content":31509,"nodeType":12829},{},[31510],{"data":31511,"content":31512,"nodeType":1921},{},[31513],{"data":31514,"marks":31515,"value":31516,"nodeType":1907},{},[],"Each site then caches a consistent view of a path’s top-level metadata - such that any site can consistently search through the namespace by starting from a strictly-consistent point. Additional buffering happens for read operations using local caching, global prefetch and tools to pre-warm cache for applications that will require low time-to-first-byte.",{"data":31518,"content":31519,"nodeType":12829},{},[31520],{"data":31521,"content":31522,"nodeType":1921},{},[31523],{"data":31524,"marks":31525,"value":31526,"nodeType":1907},{},[],"Finally, we’ve implemented a new approach to consistency management that eliminates the challenges of global consistency management by rethinking where lock enforcement happens. 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Every customer we talk with about “doing cloud right” spends much time talking about replatforming applications and going cloud native. Cloud strategies either fail to launch or fail to be agile when customers run up against two laws of gravitation:",{"data":31581,"content":31582,"nodeType":12952},{},[31583,31593],{"data":31584,"content":31585,"nodeType":12829},{},[31586],{"data":31587,"content":31588,"nodeType":1921},{},[31589],{"data":31590,"marks":31591,"value":31592,"nodeType":1907},{},[],"Data has gravity, which is compounded by egress toll gates imposed by cloud vendors",{"data":31594,"content":31595,"nodeType":12829},{},[31596],{"data":31597,"content":31598,"nodeType":1921},{},[31599],{"data":31600,"marks":31601,"value":31602,"nodeType":1907},{},[],"APIs create gravity, by requiring applications to support to platform-specific APIs",{"data":31604,"content":31605,"nodeType":1921},{},[31606,31610,31615],{"data":31607,"marks":31608,"value":31609,"nodeType":1907},{},[],"When we started building ",{"data":31611,"marks":31612,"value":31614,"nodeType":1907},{},[31613],{"type":12906},"the VAST DataSpace",{"data":31616,"marks":31617,"value":31618,"nodeType":1907},{},[],", one of the aims of this effort was to also allow customers to extend namespaces across different public cloud platforms. How do we do it? Today we’re announcing support for AWS, Azure and Google - I’m sure we’ll add others as customers express interest in other cloud environments.",{"data":31620,"content":31621,"nodeType":1921},{},[31622],{"data":31623,"marks":31624,"value":31625,"nodeType":1907},{},[],"One of the leading cloud vendors once asked me why we’re building support for their platform when they already have different point solutions that (in total) compete to varying degrees with our offering. The answer was easy: choice and fit.",{"data":31627,"content":31628,"nodeType":1921},{},[31629],{"data":31630,"marks":31631,"value":31632,"nodeType":1907},{},[],"By adding support for leading public clouds, the DataSpace creates a platform abstraction where applications no longer fight against API or data gravity. You can just run your applications using a common set of data APIs on the platform you choose, whenever you choose. No refactoring. No lock-in. ",{"data":31634,"content":31635,"nodeType":1921},{},[31636],{"data":31637,"marks":31638,"value":31639,"nodeType":1907},{},[],"Now - as for “fit”. What we’re also finding is that many of the toolsets offered in leading cloud platforms (unstructured data systems, structured data platforms) were almost all built in an era that pre-dates deep learning, and they often lean on hard drive-based constructs as well as other architectural decisions that result in all of the tradeoffs VAST is trying to break. So with today’s announcement we’re also happy to be able to talk with customers about running AI applications in cloud against data infrastructure that is fit for purpose.",{"data":31641,"content":31642,"nodeType":1921},{},[31643],{"data":31644,"marks":31645,"value":31647,"nodeType":1907},{},[31646],{"type":12906},"One tier.\nOne database.\nOne dataset. \nOne namespace. \nOne platform.",{"data":31649,"content":31670,"nodeType":13214},{"target":31650},{"metadata":31651,"sys":31654,"fields":31661},{"tags":31652,"concepts":31653},[],[],{"space":31655,"id":31657,"type":934,"createdAt":31658,"updatedAt":31658,"environment":31659,"publishedVersion":847,"revision":1136,"locale":673},{"sys":31656},{"type":657,"linkType":658,"id":659},"2eTaM2xYkTOlCfyoBnsqpX","2023-08-01T16:44:55.796Z",{"sys":31660},{"id":665,"type":657,"linkType":666},{"title":31662,"description":2031,"file":31663},"Diagram 6",{"url":31664,"details":31665,"fileName":31669,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F2eTaM2xYkTOlCfyoBnsqpX\u002F39cc8ae4bf135fe9f37ca10e0d31c38d\u002Fdiagram_6.webp",{"size":31666,"image":31667},51996,{"width":31257,"height":31668},978,"diagram 6.webp",[],{"data":31672,"content":31692,"nodeType":13214},{"target":31673},{"metadata":31674,"sys":31677,"fields":31684},{"tags":31675,"concepts":31676},[],[],{"space":31678,"id":31680,"type":934,"createdAt":31681,"updatedAt":31681,"environment":31682,"publishedVersion":847,"revision":1136,"locale":673},{"sys":31679},{"type":657,"linkType":658,"id":659},"8w4mklXsCF06Py4SHlPUv","2023-08-01T16:44:00.702Z",{"sys":31683},{"id":665,"type":657,"linkType":666},{"title":31685,"description":2031,"file":31686},"Diagram 5",{"url":31687,"details":31688,"fileName":31691,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F8w4mklXsCF06Py4SHlPUv\u002F8b527a286ee673419d7fe59bdd0a4856\u002Fdiagram_5.webp",{"size":31689,"image":31690},59210,{"width":31257,"height":31668},"diagram 5.webp",[],{"data":31694,"content":31695,"nodeType":12356},{},[31696],{"data":31697,"marks":31698,"value":31700,"nodeType":1907},{},[31699],{"type":12906},"Collapsing Application Definitions",{"data":31702,"content":31703,"nodeType":1921},{},[31704],{"data":31705,"marks":31706,"value":31707,"nodeType":1907},{},[],"OK, this last one can take a lot of twists and turns but I’m now in too-verbose territory so I’m going to keep it simple.",{"data":31709,"content":31710,"nodeType":1921},{},[31711],{"data":31712,"marks":31713,"value":31714,"nodeType":1907},{},[],"Since the dawn of computational data analytics (I’m going back to the late 1970s here), enterprise software architecture has largely fallen into one of two camps:",{"data":31716,"content":31717,"nodeType":12952},{},[31718,31728],{"data":31719,"content":31720,"nodeType":12829},{},[31721],{"data":31722,"content":31723,"nodeType":1921},{},[31724],{"data":31725,"marks":31726,"value":31727,"nodeType":1907},{},[],"Event-Centric Systems are largely responsible for capturing and processing data streams as they happen. Event architectures are tightly related to transaction processing and messaging systems and typically limit the ability to analyze data in a system of record. Since data lakes and data warehouses have never possessed the ability to handle scalable transactions, event architectures typically only support correlative analysis of real-time data against a limited data set that can fit in an event queue. Today, Apache Kafka is the standard for open and scalable event processing.",{"data":31729,"content":31730,"nodeType":12829},{},[31731],{"data":31732,"content":31733,"nodeType":1921},{},[31734],{"data":31735,"marks":31736,"value":31737,"nodeType":1907},{},[],"Data-Centric Systems, on the other hand, are designed to capture and harvest insights from large datasets… datasets so large that they would overwhelm a conventional database or event bus architecture. Datasets that need to be queried using tools optimized for deep data analysis (typically columnar data orientation) using data-parallel analytics methods across a wide collection of computers. Popular data-centric programming languages include SQL and R.",{"data":31739,"content":31740,"nodeType":1921},{},[31741],{"data":31742,"marks":31743,"value":31744,"nodeType":1907},{},[],"The implicit tradeoff in all of this is that applications can’t run deep correlative analysis against events that are happening in real-time, nor can deep analytics infrastructure process events in real time. The result is a dichotomy between systems that requires organizations to integrate disparate systems that all step down to their lowest common denominator while also forfeiting the opportunity to marry real-time and big data.",{"data":31746,"content":31747,"nodeType":1921},{},[31748,31752,31757,31761,31770],{"data":31749,"marks":31750,"value":31751,"nodeType":1907},{},[],"Today, we’re announcing the final element of the VAST Data Platform story, ",{"data":31753,"marks":31754,"value":31756,"nodeType":1907},{},[31755],{"type":12906},"the VAST Data Engine",{"data":31758,"marks":31759,"value":31760,"nodeType":1907},{},[],". The engine is the last piece of the ",{"data":31762,"content":31764,"nodeType":1916},{"uri":31763},"https:\u002F\u002Fvastdata.com\u002Fblog\u002Fthe-quest-to-build-thinking-machines",[31765],{"data":31766,"marks":31767,"value":31769,"nodeType":1907},{},[31768],{"type":15085},"‘thinking machine’",{"data":31771,"marks":31772,"value":31773,"nodeType":1907},{},[]," puzzle that refines raw unstructured data into insights that can be discovered by structured query language via the VAST DataBase. The VAST Engine is a computational framework that runs in containers across the VAST DataSpace. More than just a container stack, the Engine introduces support for triggering functions across a dynamically scalable set of CPU, GPU and DPU resources from edge to cloud.",{"data":31775,"content":31776,"nodeType":1921},{},[31777],{"data":31778,"marks":31779,"value":31780,"nodeType":1907},{},[],"The DataEngine will ship in 2024, and we’ll be unveiling a lot more details as the offering gets closer to general availability, but today I’d like to preview a capability that I just call VAST Streams (actual name: TBD). The Engine not only creates a functional programming environment to refine data through deep learning training and inference functions, but it also extends the type of data ingest that the system can support to also provide a Kafka connector that will allow data to be topics to be captured as real-time tables.",{"data":31782,"content":31783,"nodeType":1921},{},[31784],{"data":31785,"marks":31786,"value":31787,"nodeType":1907},{},[],"How do we do it? The DASE architecture is built with a persistent write buffer that allows a batch of messages to be written directly into a VAST table in record form. As mentioned above, a background process then converts these records into a flash-optimized columnar object format that makes it easy to run accelerated queries at any scale. Triggers then call functions as data is streamed into a topic, and event streams just become another source of data that can create a continuous loop of data enrichment and processing in the VAST Data Platform.",{"data":31789,"content":31790,"nodeType":1921},{},[31791],{"data":31792,"marks":31793,"value":31794,"nodeType":1907},{},[],"With this, we hope to break down decades-old application definitions to build systems that can interact with the natural world and create real time associations, realizations and discoveries by comparing new experiences with past learnings.",{"data":31796,"content":31797,"nodeType":1921},{},[31798],{"data":31799,"marks":31800,"value":31802,"nodeType":1907},{},[31801],{"type":12906},"One tier.\nOne database.\nOne dataset. \nOne namespace.\nOne platform.\nOne perpetual learning 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Era","the-end-of-the-unstructured-data-era","2024-10-01",{"metadata":32039,"sys":32042,"fields":32051},{"tags":32040,"concepts":32041},[],[],{"space":32043,"id":32045,"type":14,"createdAt":32046,"updatedAt":32046,"environment":32047,"publishedVersion":901,"revision":1136,"contentType":32049,"locale":673},{"sys":32044},{"type":657,"linkType":658,"id":659},"p8OE8vmOGlxIcoHaRxicV","2024-09-27T14:34:50.129Z",{"sys":32048},{"id":665,"type":657,"linkType":666},{"sys":32050},{"type":657,"linkType":671,"id":12173},{"entryTitle":32035,"image":32052},{"metadata":32053,"sys":32056,"fields":32063},{"tags":32054,"concepts":32055},[],[],{"space":32057,"id":32059,"type":934,"createdAt":32060,"updatedAt":32060,"environment":32061,"publishedVersion":847,"revision":1136,"locale":673},{"sys":32058},{"type":657,"linkType":658,"id":659},"7LDEXwQwNDs38afwZzh2sP","2024-09-27T14:34:46.516Z",{"sys":32062},{"id":665,"type":657,"linkType":666},{"title":32035,"description":32035,"file":32064},{"url":32065,"details":32066,"fileName":32069,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F7LDEXwQwNDs38afwZzh2sP\u002F5c0ec01fb525fae5fffb955e5f969a07\u002Fthe-end-of-the-unstructured-data-era.webp",{"size":32067,"image":32068},78530,{"width":1144,"height":1145},"the-end-of-the-unstructured-data-era.webp","Retrieval Augmented Generation. RAG.\n\nI’m going to attempt to push past the marketing speak and focus on a few key areas that I think are really special about what we’re doing in the space of information retrieval for AI applications. Due to the fact that there are several __fundamental__ innovations that change the definition of how enterprises will define terms such as “real time” and “enterprise-ready,” this post is a relatively long one. Buckle up.\n\nTable of Contents:\n- [Introduction to Retrieval-Augmented Generation (RAG) for AI Applications](#Introduction)\n- [Today’s Challenges with RAG](#TodaysChallengesWithRag)\n- [The VAST Data InsightEngine with NVIDIA](#InsightEngine)\n- [The End of the Unstructured Data Era](#TheEndOfTheUnstructuredDataEra)\n\n\u003Ca id=\"Introduction\">\u003C\u002Fa>In August of 2020, a team at Facebook first presented a paper on [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401). This paper introduced an idea of how to augment the information retrieval of an AI model with additional data that a model could query and retrieve from external data sources. Four years later, the market has now developed the tools and ecosystem required to establish RAG as the ‘killer app’ that can usher in the value of AI to enterprises looking to embrace faster knowledge-sharing and decision-making.\n\n\u003Ca id=\"TodaysChallengesWithRag\">\u003C\u002Fa>So what is the challenge that enterprise RAG-based systems are attempting to solve?\n\nAs organizations start to bring AI agents into their environment, it’s clear that there are things that an agent can and does know, and there are things trained AI models can’t yet accomplish. Some challenges include:\n\n- AI models are trained and fine-tuned periodically, so they may not be equipped with the latest and greatest information needed to power a business in real time.\n\n- Generative AI models rely on the accuracy of generative predictions - and they can hallucinate when they rely solely on patterns within their training dataset.\n\n- Models are also bound by the information they’ve been trained on, and optimized for the amount of memory needed in the machines you can deploy AI agents on. Therefore, AI teams will often look to minimize the size of the model and augment AI applications with external data sources (versus doing much much larger training and fine-tuning work and then fitting inference models into expensive, memory-rich machines).\n\n- Finally, AI models are not typically designed to enforce enterprise access control or permissions. Therefore, it can be impractical to train them on large enterprise datasets that have a wide variety of access permissions, since users cannot be given unfettered access to the information these models gain. Conversely, it’s inefficient to train a model per enterprise user on just the data they are permitted to see. Even if you could, permissions change more frequently than models do.\n\n\u003Cp style=\"text-wrap: pretty\">Fortunately, AI retrieval provides the answer to the above problems and paves the way for general enterprise adoption of agentic AI applications. A generalized workflow looks as follows:\u003C\u002Fp>\n\u003Cp style=\"margin-left: 2.25rem; text-indent: -2.25rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">1.\u003C\u002Fspan> An AI inference model, typically an LLM, will receive a request\u002Fprompt from some user\u003C\u002Fp>\n\u003Cp style=\"margin-left: 2.25rem; text-indent: -2.25rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">2.\u003C\u002Fspan> The model will attempt to answer the question itself, if possible, and then resort to going to external data sources when the response’s confidence score is not high enough\u003C\u002Fp>\n\u003Cp style=\"margin-left: 2.25rem; text-indent: -2.25rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">3.\u003C\u002Fspan> At this point, the model can retrieve external data typically from one of two sources\u003C\u002Fp>\n\u003Cp style=\"margin-left: 4rem; text-indent: -2rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">a.\u003C\u002Fspan> If the answers live inside an organization’s unstructured data, this will have been indexed by some AI embedding model and the context from enterprise file and object storage systems will be then stored and indexed in a vector database\u003C\u002Fli>\u003C\u002Fp>\n\u003Cp style=\"margin-left: 4rem; text-indent: -2rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">b.\u003C\u002Fspan> Alternatively, structured data that exists within enterprise data warehouses can be easily queried by an AI model using SQL\u003C\u002Fli>\u003C\u002Fp>\n\n![The End of the Unstructured Data Era](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F16Hk7EEpb2EBEdPmW8MaHY\u002Ff32d8ab5545386d401f66ae9a0d8b29a\u002Fthe-end-of-the-unstructured-data-era-1.webp)\n\nRetrieval solves for many of the key drawbacks of AI models within the enterprise.\n\n| AI Model Challenge | Retrieval Solution |\n| ---------- | ---------- |\n| Models are trained and fine-tuned periodically – their data may not be current. | Retrieval systems can source data from real-time or near-real-time sources. |\n| Models can hallucinate. | Retrieval allows AI models to not have to know everything, and to rather just serve as interpreters of queries that can source facts. |\n| Big and smart models can be expensive to deploy. | Retrieval allows for AI models to stay small while augmenting them with massive data volumes that are semantically indexed. |\n| AI models typically can’t enforce granular permissions. | Retrieval aligns the query output with individual user permissions, enabling data isolation across sensitive enterprise datasets. |\n\nOK... so, we’ve solved enterprise AI and everything’s right in the world… right? No.\nWhile there are several different approaches to retrieval in the market, none has passed our sniff test so far.\n\n__Problem #1: Real Time Is Not Really Real Time__\n\nMany vendors in the real-time RAG space talk about their ability to answer queries quickly, and don’t address that the data they’ve indexed is the product of some periodic batch process that isn’t current with what’s happening in real time in an organization.\n\nWhile datasets for retrieval can be updated more frequently than a model is fine-tuned, this process is not yet able to feed information to AI models in real time.\n- Vector embeddings are created from file data only periodically, often on a batch schedule.\n- File system ACLs are not atomic with vector databases, also synced only periodically.\n- Enterprise data warehouses (EDWs) struggle to ingest data in real time, so real-time data starts in an event bus and is difficult to query against until it is ETL’d into an EDW, which is also done as a periodic batch process.\n\nAnd all of this source data can change… data doesn’t only get expanded, data can be removed from the enterprise knowledgebase. This makes the role of retrieval systems even more complicated when they can and do expose context that is suddenly out of date or inaccurate.\n\nConsider, finally, that a human is only one type of user of an AI model. AI agents are now being combined to execute complex AI workflows and enhance each other. If a business is beginning to run at the speed of AI, it’s unthinkable that AI engines should wait minutes or days for up-to-date business data.\n\u003Cp style=\"text-wrap: pretty\">Why is building scalable + real-time systems hard? It’s the architecture (I’ll come back to this).\u003C\u002Fp>\n\n__Problem #2: Vector Database Scalability Trades Scale for Performance__\n\nAs we poll our average Fortune 500 customers, putting aside the large hyperscalers, we find customers that are sitting on 100s of petabytes of unique enterprise data. In order to appropriately index this data using AI and data vectorization tools, we’ll need systems that can store and index trillions of vector embeddings and be able to search on them in real time (in order to preserve a quality user experience). This is not the design point that the early vectorDB efforts were built around - as they rushed to get out solutions for chatbots that front-ended small datasets such as PDF document stores.\n\nSure, people today may say… ‘I only have to curate a small number of documents and feed them into a vector DB, since the embedding models require hand-holding’. At VAST, we’re not worrying about the state of AI-based data indexing as it is today. We’re watching the exponential improvements in embedding models and seeing a time in the not-too-distant future where these tools will be of a caliber that they can be used to index the entirety of an enterprise knowledge base and even help to curate enterprise data. At that point, hyper-scale vector stores and search tools are table stakes.  \n\nThe trillion-way vector problem is already a reality for large AI builders like OpenAI and Perplexity that have gone and indexed the internet. This is the scale of product we want to build… for everyone.\n\nFinally, you won’t just need to read from vector search engines in constant time — you’ll also need to be able to create, store and index embeddings in real time. At this point, we find ourselves back in the place we were with problem #1… architecture is a core limiter for being able to scale datasets and transacting into them with high performance when technologies lean on the classic shared-nothing systems designs.\n\n__Problem #3: It’s All Just Kinda Messy__\n\nRAG pipelines involve many steps:\n- Set up a system alongside your primary file and object storage systems that can support AI-based indexing, since legacy file systems don’t handle AI parallel processing well. If you want to use a cloud-based data platform, just copy all of your enterprise file systems into some cloud storage bucket.\n- Then build some GPU cluster to periodically scan and create these embeddings, with all of the workflow orchestration and gluecode to make this automatic.\n- Then build a vector database that can store and index these embeddings, as well as help build knowledge graphs. Stamp these out into silos of information insight as you hit scaling walls.\n- Remember, to save money, you should also tier your embeddings off to some slow object storage with the intent of never having to query against that data.\n- Then build an enterprise data warehouse to support your Text-to-SQL queries.\n- But to do this, you’ll also need to build an event bus that captures real-time event streams since the data warehouse is not transactional, hoping you don’t need to correlate the two at the same time.\n- Then try your best to establish unified policy enforcement across a bunch of independent systems and hope that permissions don’t fall too far out of sync that anyone notices.\n- And finally, spend the next three years trying to tune this infrastructure so that you can get as close to real time as possible so that your business isn’t continually getting served old or stale data.\n\n![Before InsightEngine](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1fCkPWGVWwoy5qS9iDfv94\u002F8b202d4f288fdded1941f7a04bfc3047\u002Fbefore.gif)\n\n##### AI Retrieval Problems: The Big Takeaway\n\nWith respect to these pipelines… I believe the world has been doing the wrong thing.\n\nThe enterprise just wants more intelligence from file and object storage. I think of vectors and knowledge graphs as just higher forms of file system metadata… why wouldn’t we want this to run natively from within the file system if it was possible? This would solve the problems of data and metadata atomicity and make it easy for AI agents to make data conversational, talking to storage almost as a new protocol. In order for this to happen, a file system would need support for real-time transactional and analytical metadata analytics, a job engine, event triggers, etc.  Well, that’s a data platform.\n\nAnd why wouldn’t we want real-time transactional data warehouses?  \nOf course we do. It’s just not been possible… until now.\n\n![VAST InsightEngine With NVIDIA](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5w9s4lVrMgDog3W8v5YDiK\u002F9fc7d0fa5ffb5970fbb1fd6f1ba57ede\u002Fvast-insightengine-nvidia.webp)\n\n\u003Ca id=\"InsightEngine\">\u003C\u002Fa>\n#### Introducing the InsightEngine\n\nThis is why, today, we’re delighted to take the covers of our collaboration with an AI leader and introduce the VAST Data InsightEngine with NVIDIA.\n\nVAST InsightEngine with NVIDIA is an all-encompassing system to support AI Retrieval that allows organizations to ingest all of their structured and unstructured data streams in real time; to index them using AI embedding tools in real time; and to serve answers to agentic applications with real-time search tools that work at any scale, and with the enterprise security that comes naturally from building intelligence into an enterprise data foundation. It’s fast, scalable, secure and simple.\n\nVAST InsightEngine with NVIDIA is designed to run [NVIDIA NIM](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fai\u002F) microservices, part of the [NVIDIA AI Enterprise](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fproducts\u002Fai-enterprise\u002F) software platform, natively from within the VAST Data Platform, leveraging VAST containers that support NVIDIA accelerated computing for AI acceleration. Inside the system, real-time workflows are built into the [VAST DataEngine](..\u002F..\u002Fplatform\u002Fdataengine#) that leverage native application triggers that automatically create embeddings on data and update vector indexes as new unstructured data comes into the system. To start, we’ll support NVIDIA NeMo Retriever NIM microservices that create embeddings from text and video, and others will be added in the future.\n\nNow, it’s one thing to create a separate storage space for all of your AI Retrieval data… this, to us, is crazy. This is why, from the earliest days, we’ve been working on building an enterprise scale-out NAS and object storage system (among other things) that is Universal in nature. We don’t want customers to take their enterprise data to some GPUs — we want them to bring GPUs to their enterprise data that is on AI-ready infrastructure… so that intelligent applications can benefit from getting the broadest access to all of an organization’s unstructured data.\n\n![VAST DASE NIMS NVIDIA](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5hSGdxw2J6ToA4BrpJzICc\u002F42ac135f7f0e6c9701af690b922e8c24\u002FVAST-DASE-NVIDIA-NIMS.webp)\n\nTo enable semantic search of this unstructured data that lives in the [VAST DataStore](..\u002F..\u002Fplatform\u002Fdatastore#), we’re proud to announce that the [VAST DataBase](..\u002F..\u002Fblog\u002Fblurring-the-lines-between-event-driven-and-data-driven-architectures) has been extended to support a new data type: vector embeddings. Thanks to the scalability of the [VAST DASE architecture](..\u002F..\u002Fwhitepaper\u002F#TheDisaggregatedSharedEverythingArchitecture), we can now build table stores that support trillions of vectors, making it possible to index an entire enterprise. But scale is only one challenge — we also needed to make these data structures easy to update and query in real time.\n\nAs many of you know, VAST has been working with [Similarity algorithms](..\u002F..\u002Fblog\u002Fbreaking-data-reduction-trade-offs-with-global-compression) for almost 10 years now (to optimize data reduction and make flash as affordable as HDD storage), and it turns out that these same algorithms are now very popular to help with searching large vector spaces. The VAST DASE architecture pioneers new ideas around transactional parallelism and provides all of the system’s containers the ability to share a common, real-time view of a namespace organized across low-cost, low-latency flash. The result is a system that makes it possible to transact millions of events per second and to update and search indices in milliseconds.\n\n![VAST DASE 2024](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6A8CeAOEvcOUIsnoRt9sOr\u002F629992ade5df231d4e6166499c442cc8\u002Fvast-dase.webp)\n\nSecurity-wise, the platform will pull permissions and policy data up into the VAST DataBase, such that every vector search happens against permissions data that is atomically synced with the data source. No more do you need to sync independent permissions systems. Permissions are enforced and updated in real time thanks to the transactional nature of the VAST DataBase and unified data management.\n\nFinally, for SQL-to-Text operations, we’re also proud to announce that the VAST DataBase now also supports a Kafka-compatible event broker. This is a seemingly small footnote on an otherwise very long blog… but the effects of this are going to be profound across the data science community as we take the world to a place where you no longer need an external event bus to capture enterprise event streams. Now, you can stream directly into VAST DataBase tables, where event data is then instantly part of a (predominantly) columnar query.\n\n\u003Ca id=\"TheEndOfTheUnstructuredDataEra\">\u003C\u002Fa>\n#### The End of the Unstructured Data Era\n\nLooking into the future, AI will become increasingly embedded into core business applications. File systems and object storage, alone, force IT teams to build cumbersome retrieval pipelines and wrestle with the complexity of stale data, stale permissions and a lot of integration and gluecode headaches. Most importantly, these pipelines are not working as fast as AI engines enable us to. The idea of a standalone file system is fading as new business priorities need more from data infrastructure.\n\nAt VAST, we want to store, index and serve the world’s data to tomorrow’s engines of discovery.  To get there, our simple mission is to eliminate infrastructure constraints, bottlenecks and tradeoffs. This mission forces us to think further up the stack, and to challenge the conceptions of how systems should be built to realize the greatest gains. VAST InsightEngine with NVIDIA is the culmination of what we’ve been working toward for almost a decade and underscores the need for unstructured data to be treated as a first-class citizen in the age of Enterprise AI. But unstructured data, alone, is not enough.\n\n\u003Cvideo autoplay loop muted poster=\"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1giyUEcaPo9m0SvSxM5YxY\u002Ff46de3ba18a833d5233f975f3e9c21bf\u002FInsight-Engine-Gif_comp.webp\" style=\"border-radius: 20px\">\n  \u003Csource src=\"\u002F\u002Fvideos.ctfassets.net\u002F2f3meiv6rg5s\u002F2lJKo9dNuBR8xdkMuRZA4U\u002F85a470ba8f1bd5bcef811f0852c0baf9\u002FInsight-Engine-Gif_comp.mp4\" type=\"video\u002Fmp4\" \u002F>\n\u003C\u002Fvideo>\n\nFiles, objects, tables, triggers… and now AI workflows that run natively in our Engine.\nWe look forward to sharing more as we get closer to product launch in early ’25.\n\nThanks for reading.\n\nJeff\n",[32072,32097,32122,32147,32172],{"metadata":32073,"sys":32076,"fields":32083},{"tags":32074,"concepts":32075},[],[],{"space":32077,"id":12571,"type":14,"createdAt":12572,"updatedAt":12573,"environment":32079,"publishedVersion":12576,"revision":12577,"contentType":32081,"locale":673},{"sys":32078},{"type":657,"linkType":658,"id":659},{"sys":32080},{"id":665,"type":657,"linkType":666},{"sys":32082},{"type":657,"linkType":671,"id":922},{"entryTitle":942,"title":942,"url":12581,"image":32084},{"metadata":32085,"sys":32088,"fields":32093},{"tags":32086,"concepts":32087},[],[],{"space":32089,"id":12589,"type":934,"createdAt":12590,"updatedAt":12591,"environment":32091,"publishedVersion":12594,"revision":6276,"locale":673},{"sys":32090},{"type":657,"linkType":658,"id":659},{"sys":32092},{"id":665,"type":657,"linkType":666},{"title":677,"description":677,"file":32094},{"url":12597,"details":32095,"fileName":12602,"contentType":949},{"size":12599,"image":32096},{"width":12601,"height":12601},{"metadata":32098,"sys":32101,"fields":32108},{"tags":32099,"concepts":32100},[],[],{"space":32102,"id":12610,"type":14,"createdAt":12611,"updatedAt":12612,"environment":32104,"publishedVersion":12615,"revision":1727,"contentType":32106,"locale":673},{"sys":32103},{"type":657,"linkType":658,"id":659},{"sys":32105},{"id":665,"type":657,"linkType":666},{"sys":32107},{"type":657,"linkType":671,"id":922},{"entryTitle":697,"title":697,"image":32109},{"metadata":32110,"sys":32113,"fields":32118},{"tags":32111,"concepts":32112},[],[],{"space":32114,"id":12626,"type":934,"createdAt":12627,"updatedAt":12628,"environment":32116,"publishedVersion":12631,"revision":12632,"locale":673},{"sys":32115},{"type":657,"linkType":658,"id":659},{"sys":32117},{"id":665,"type":657,"linkType":666},{"title":697,"description":697,"file":32119},{"url":12635,"details":32120,"fileName":12639,"contentType":949},{"size":12637,"image":32121},{"width":12601,"height":12601},{"metadata":32123,"sys":32126,"fields":32133},{"tags":32124,"concepts":32125},[],[],{"space":32127,"id":12647,"type":14,"createdAt":12648,"updatedAt":12649,"environment":32129,"publishedVersion":12631,"revision":12632,"contentType":32131,"locale":673},{"sys":32128},{"type":657,"linkType":658,"id":659},{"sys":32130},{"id":665,"type":657,"linkType":666},{"sys":32132},{"type":657,"linkType":671,"id":922},{"entryTitle":12655,"title":12655,"url":12656,"image":32134},{"metadata":32135,"sys":32138,"fields":32143},{"tags":32136,"concepts":32137},[],[],{"space":32139,"id":12664,"type":934,"createdAt":12665,"updatedAt":12666,"environment":32141,"publishedVersion":7383,"revision":12632,"locale":673},{"sys":32140},{"type":657,"linkType":658,"id":659},{"sys":32142},{"id":665,"type":657,"linkType":666},{"title":12655,"description":12655,"file":32144},{"url":12671,"details":32145,"fileName":12676,"contentType":949},{"size":12673,"image":32146},{"width":12601,"height":12675},{"metadata":32148,"sys":32151,"fields":32158},{"tags":32149,"concepts":32150},[],[],{"space":32152,"id":12684,"type":14,"createdAt":12685,"updatedAt":12686,"environment":32154,"publishedVersion":12631,"revision":12632,"contentType":32156,"locale":673},{"sys":32153},{"type":657,"linkType":658,"id":659},{"sys":32155},{"id":665,"type":657,"linkType":666},{"sys":32157},{"type":657,"linkType":671,"id":922},{"entryTitle":797,"title":797,"url":777,"image":32159},{"metadata":32160,"sys":32163,"fields":32168},{"tags":32161,"concepts":32162},[],[],{"space":32164,"id":12699,"type":934,"createdAt":12700,"updatedAt":12701,"environment":32166,"publishedVersion":1727,"revision":12632,"locale":673},{"sys":32165},{"type":657,"linkType":658,"id":659},{"sys":32167},{"id":665,"type":657,"linkType":666},{"title":797,"description":797,"file":32169},{"url":12706,"details":32170,"fileName":12710,"contentType":949},{"size":12708,"image":32171},{"width":12601,"height":12601},{"metadata":32173,"sys":32176,"fields":32183},{"tags":32174,"concepts":32175},[],[],{"space":32177,"id":12718,"type":14,"createdAt":12719,"updatedAt":12720,"environment":32179,"publishedVersion":1727,"revision":12723,"contentType":32181,"locale":673},{"sys":32178},{"type":657,"linkType":658,"id":659},{"sys":32180},{"id":665,"type":657,"linkType":666},{"sys":32182},{"type":657,"linkType":671,"id":922},{"entryTitle":12727,"title":12728,"image":32184},{"metadata":32185,"sys":32188,"fields":32193},{"tags":32186,"concepts":32187},[],[],{"space":32189,"id":12736,"type":934,"createdAt":12737,"updatedAt":12738,"environment":32191,"publishedVersion":12741,"revision":12742,"locale":673},{"sys":32190},{"type":657,"linkType":658,"id":659},{"sys":32192},{"id":665,"type":657,"linkType":666},{"title":657,"description":657,"file":32194},{"url":12745,"details":32195,"fileName":12749,"contentType":949},{"size":12747,"image":32196},{"width":12601,"height":12601},{"metadata":32198,"sys":32201,"fields":32211},{"tags":32199,"concepts":32200},[],[],{"space":32202,"id":32204,"type":14,"createdAt":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Data InsightEngine with NVIDIA transforms AI workflows. 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